validations, figures, text finalisation started

50 files changed, 2933 insertions, 2009 deletions

diff --git a/paper/Makefile b/paper/Makefile
index d66bf36..4aa5ab3 100644
--- a/paper/Makefile
+++ b/paper/Makefile
@@ -1,51 +1,55 @@
 # Paper
-loael.pdf: loael.md functional-groups.pdf loael-dataset-correlation.pdf rmse.R test-set-validation.csv crossvalidations.R data/common-median.csv
+loael.pdf: loael.md references.bibtex
+	pandoc -r markdown+simple_tables+table_captions+yaml_metadata_block -s -S --bibliography=references.bibtex --latex-engine=pdflatex --filter pandoc-crossref --filter pandoc-citeproc -o loael.pdf loael.md
 
-loael.md: loael.Rmd rmse.R
+loael.md: loael.Rmd figures validations
+	Rscript --vanilla -e "library(knitr); knit('loael.Rmd');"
 
-loael.docx: loael.md functional-groups.pdf loael-dataset-correlation.pdf
+loael.docx: loael.md 
 	pandoc --filter pandoc-crossref --filter pandoc-citeproc loael.md -s -o loael.docx
 
 rmse.R: rmse.rb
 	ruby rmse.rb
 
-crossvalidations.R: crossvalidations.rb
-	ruby crossvalidations.rb
+# Figures
 
-test-set-validation.csv: test-set-validation.rb
-	ruby test-set-validation.rb
+figures: datasets validations figure/functional-groups.pdf  figure/test-prediction.pdf figure/test-correlation.pdf figure/crossvalidation.pdf
 
-%.md: %.Rmd
-	Rscript --vanilla -e "library(knitr); knit('$<');"
+figure/functional-groups.pdf: data/functional-groups-reduced4R.csv functional-groups.R
+	Rscript functional-groups.R
 
-%.pdf: %.md
-	pandoc --filter pandoc-crossref --filter pandoc-citeproc $< -s -o $@
+figure/crossvalidation.pdf: data/mazzatorta-cv.csv data/swiss-cv.csv data/combined-cv.csv 
+	Rscript crossvalidation-plots.R
 
-functional-groups.pdf: functional-groups-reduced4R.csv functional-groups.R
-	R CMD BATCH functional-groups.R
+figure/test-prediction.pdf: data/mazzatorta-test-predictions.csv data/swiss-test-predictions.csv data/combined-test-predictions.csv data/median-correlation.csv  test-prediction-plot.R
+	Rscript test-prediction-plot.R
 
-loael-dataset-correlation.pdf: loael-dataset-comparison.rb
-	ruby loael-dataset-comparison.rb
+figure/test-correlation.pdf: data/mazzatorta-test-predictions.csv data/swiss-test-predictions.csv data/combined-test-predictions.csv data/median-correlation.csv  test-correlation-plot.R
+	Rscript test-correlation-plot.R
 
-#data/common-test.csv: create-test-set.rb
-	#ruby create-test-set.rb
+# Validations
 
-dataset-correlation.R: data/common-test.csv dataset-correlation.R
-	Rscript dataset-correlation.R
+validations: test-predictions crossvalidations
 
-# Validations
+crossvalidations: data/mazzatorta-cv.csv data/swiss-cv.csv data/combined-cv.csv
+
+data/mazzatorta-cv.csv: crossvalidation.rb data/mazzatorta.csv
+	ruby crossvalidation.rb mazzatorta.csv
+
+data/swiss-cv.csv: crossvalidation.rb data/swiss.csv
+	ruby crossvalidation.rb swiss.csv
 
-crossvalidation.R: data/mazzatorta.csv,data/swiss.csv,data/combined.csv crossvalidation.rb
-	crossvalidation.rb
+data/combined-cv.csv: crossvalidation.rb data/combined.csv
+	ruby crossvalidation.rb combined.csv
 
 test-predictions: data/mazzatorta-test-predictions.csv data/swiss-test-predictions.csv data/combined-test-predictions.csv 
 
-data/mazzatorta-test-predictions.csv: test-set-validation.rb data/test.csv data/mazzatorta.csv 
-	ruby test-set-validation.rb mazzatorta.csv
-data/swiss-test-predictions.csv: test-set-validation.rb data/test.csv data/swiss.csv
-	ruby test-set-validation.rb swiss.csv
-data/combined-test-predictions.csv: test-set-validation.rb data/test.csv data/combined.csv
-	ruby test-set-validation.rb combined.csv
+data/mazzatorta-test-predictions.csv: test-validation.rb data/test.csv data/mazzatorta.csv 
+	ruby test-validation.rb mazzatorta.csv
+data/swiss-test-predictions.csv: test-validation.rb data/test.csv data/swiss.csv
+	ruby test-validation.rb swiss.csv
+data/combined-test-predictions.csv: test-validation.rb data/test.csv data/combined.csv
+	ruby test-validation.rb combined.csv
 
 # Datasets
 
diff --git a/paper/Rakefile b/paper/Rakefile
deleted file mode 100644
index 1d45b9e..0000000
--- a/paper/Rakefile
+++ /dev/null
@@ -1,12 +0,0 @@
-task :default => "loael.pdf"
-
-file "loael.pdf" => ["loael.md","functional-groups.pdf", "loael-dataset-correlation.pdf"]
-  `pandoc --filter pandoc-citeproc loael.md -s -o loael.pdf`
-  pid=`pidof mupdf`.chomp
-  `kill -s SIGHUP #{pid}`
-
-file "functional-groups.pdf" => ["functional-groups-reduced4R.csv","functional-groups.R"]
-  `R CMD BATCH functional-groups.R`
-
-file "loael-dataset-correlation.pdf" => ["loael-dataset-comparison.rb"]
-  `ruby loael-dataset-comparison.rb`
diff --git a/paper/appendix/functional-groups.csv b/paper/appendix/functional-groups.csv
deleted file mode 120000
index ce02e14..0000000
--- a/paper/appendix/functional-groups.csv
+++ /dev/null
@@ -1 +0,0 @@
-functional-groups.csv
\ No newline at end of file
diff --git a/paper/create-median-correlation.rb b/paper/create-median-correlation.rb
index 6aeee11..9a2f6f5 100644
--- a/paper/create-median-correlation.rb
+++ b/paper/create-median-correlation.rb
@@ -16,13 +16,13 @@ common_compound_ids.each do |cid|
     new_values -= identical
   end
   unless old_values.empty? or new_values.empty?
-    data << [c.smiles,old_values.mean,new_values.mean]
+    data << [c.smiles,old_values.median,new_values.median]
   end
 end
 
 data.sort!{|a,b| a[1] <=> b[1]}
 
-CSV.open(File.join(DATA,"common-median.csv"),"w+") do |csv|
+CSV.open(File.join(DATA,"median-correlation.csv"),"w+") do |csv|
   csv << ["SMILES","mazzatorta","swiss"]
   data.each{|r| csv << r}
 end
diff --git a/paper/crossvalidation-plots.R b/paper/crossvalidation-plots.R
new file mode 100644
index 0000000..6665fdb
--- /dev/null
+++ b/paper/crossvalidation-plots.R
@@ -0,0 +1,20 @@
+library(ggplot2)
+library(grid)
+library(gridExtra)
+
+mazzatorta = read.csv("data/mazzatorta-cv.csv",header=T)
+swiss = read.csv("data/swiss-cv.csv",header=T)
+combined = read.csv("data/combined-cv.csv",header=T)
+
+#experimental <- read.csv("data/median-correlation.csv",header=T)
+#p1 = qplot(-log10(mazzatorta),-log10(swiss),data=experimental,xlab="-log10(LOAEL Mazzatorta median)",ylab="-log10(LOAEL Swiss Federal Office median)",main="Experimental data") + geom_point() + geom_abline(intercept=0.0) + xlim(-2,4.5) + ylim(-2,4.5)
+
+p2 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=mazzatorta,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Mazzatorta") + geom_point() + geom_abline(intercept=0.0)  + xlim(-2,4.5) + ylim(-2,4.5)
+
+p3 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=swiss,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Swiss Federal Office") + geom_point() + geom_abline(intercept=0.0)  + xlim(-2,4.5) + ylim(-2,4.5)
+
+p4 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=combined,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0)  + xlim(-2,4.5) + ylim(-2,4.5)
+
+pdf('figure/crossvalidation.pdf')
+grid.arrange(p2,p3,p4,ncol=2)
+dev.off()
diff --git a/paper/crossvalidation.R b/paper/crossvalidation.R
new file mode 100644
index 0000000..a32f608
--- /dev/null
+++ b/paper/crossvalidation.R
@@ -0,0 +1,16 @@
+mazzatorta = read.csv("data/mazzatorta-cv.csv",header=T)
+swiss = read.csv("data/swiss-cv.csv",header=T)
+combined = read.csv("data/combined-cv.csv",header=T)
+
+cv.mazzatorta.p = round(cor.test(-log(mazzatorta$LOAEL_measured_median),-log(mazzatorta$LOAEL_predicted))$p.value,2)
+cv.mazzatorta.r_square = round(cor(-log(mazzatorta$LOAEL_measured_median),-log(mazzatorta$LOAEL_predicted))^2,2)
+cv.mazzatorta.rmse = round(sqrt(mean((-log(mazzatorta$LOAEL_measured_median)+log(mazzatorta$LOAEL_predicted))^2)),2)
+
+cv.swiss.p = round(cor.test(-log(swiss$LOAEL_measured_median),-log(swiss$LOAEL_predicted))$p.value,2)
+cv.swiss.r_square = round(cor(-log(swiss$LOAEL_measured_median),-log(swiss$LOAEL_predicted))^2,2)
+cv.swiss.rmse = round(sqrt(mean((-log(swiss$LOAEL_measured_median)+log(swiss$LOAEL_predicted))^2)),2)
+
+cv.combined.p = round(cor.test(-log(combined$LOAEL_measured_median),-log(combined$LOAEL_predicted))$p.value,2)
+cv.combined.r_square = round(cor(-log(combined$LOAEL_measured_median),-log(combined$LOAEL_predicted))^2,2)
+cv.combined.rmse = round(sqrt(mean((-log(combined$LOAEL_measured_median)+log(combined$LOAEL_predicted))^2)),2)
+
diff --git a/paper/crossvalidation.rb b/paper/crossvalidation.rb
new file mode 100644
index 0000000..1f4c023
--- /dev/null
+++ b/paper/crossvalidation.rb
@@ -0,0 +1,23 @@
+require_relative 'include.rb'
+
+name = File.basename ARGV[0], ".csv"
+file = File.join DATA,ARGV[0]
+csv_file = File.join(DATA,ARGV[0].sub(/.csv/,'-cv.csv'))
+id_file = File.join(DATA,ARGV[0].sub(/.csv/,'-cv.id'))
+dataset = Dataset.from_csv_file file
+model = Model::LazarRegression.create dataset
+cv = RegressionCrossValidation.create model
+File.open(id_file,"w+"){|f| f.puts cv.id}
+
+data = []
+cv.predictions.each do |p|
+  smi = Compound.find(p[0]).smiles
+  data << [smi,p[1].median,p[2],p[3]]
+end
+
+data.sort!{|a,b| a[1] <=> b[1]}
+
+CSV.open(csv_file,"w+") do |csv|
+  csv << ["SMILES","LOAEL_measured_median","LOAEL_predicted","Confidence"]
+  data.each{|r| csv << r}
+end
diff --git a/paper/crossvalidations.R b/paper/crossvalidations.R
deleted file mode 100644
index cdc4c7e..0000000
--- a/paper/crossvalidations.R
+++ /dev/null
@@ -1,9 +0,0 @@
-cv.mazzatorta.rmse <- 0.8439115008205602
-cv.mazzatorta.r.squared <- 0.3730663179459023
-cv.mazzatorta.mae <- 0.6546182843884356
-cv.swiss.rmse <- 0.7507139457130771
-cv.swiss.r.squared <- 0.2507741939299348
-cv.swiss.mae <- 0.6068918271561476
-cv.combined.rmse <- 1.4536329017938434
-cv.combined.r.squared <- 0.12100621490895397
-cv.combined.mae <- 1.2096846143410287
diff --git a/paper/crossvalidations.rb b/paper/crossvalidations.rb
deleted file mode 100644
index f6a5143..0000000
--- a/paper/crossvalidations.rb
+++ /dev/null
@@ -1,18 +0,0 @@
-require_relative 'include.rb'
-file = File.join(DATA,ARGV[0])
-dataset = Dataset.from_csv_file file
-model = Model::LazarRegression.create dataset
-cv = RegressionCrossValidation.create model
-=begin
-=end
-
-datasets = ["mazzatorta","swiss","combined"]
-File.open("crossvalidations.R","w+") do |f|
-  [0,1,5].each do |i|
-    dataset = datasets.shift
-    cv = OpenTox::RegressionCrossValidation.all[i]
-    f.puts "cv.#{dataset}.rmse <- #{cv.rmse}"
-    f.puts "cv.#{dataset}.r.squared <- #{cv.r_squared}"
-    f.puts "cv.#{dataset}.mae <- #{cv.mae}"
-  end
-end
diff --git a/paper/SMARTS_InteLigand.txt b/paper/data/SMARTS_InteLigand.txt
index 23bc6e2..23bc6e2 100644
--- a/paper/SMARTS_InteLigand.txt
+++ b/paper/data/SMARTS_InteLigand.txt
diff --git a/paper/data/combined-cv.csv b/paper/data/combined-cv.csv
new file mode 100644
index 0000000..4de9b72
--- /dev/null
+++ b/paper/data/combined-cv.csv
@@ -0,0 +1,923 @@
+SMILES,LOAEL_measured_median,LOAEL_predicted,Confidence
+ClC12C3C4(C(C1(Cl)Cl)(C1(C2(C3(Cl)C(C41Cl)(Cl)Cl)Cl)Cl)Cl)Cl,1.9565721591442926e-05,0.0014218133641616987,0.625
+ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C=C2)(Cl)Cl)Cl,2.7404023436797774e-05,0.0012794179116857743,1
+ClC1C2OC2C2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,6.421500622500271e-05,0.0006312904946719587,1
+N#Cc1nn(c(c1S(=O)C(F)(F)F)N)c1c(Cl)cc(cc1Cl)C(F)(F)F,0.00013496580117055152,0.034974567888840846,1
+N#Cc1nn(c(c1S(=O)C(F)(F)F)N)c1c(Cl)cc(cc1Cl)C(F)(F)F,0.0001372533562906347,0.02698619904377642,1
+CCSCCSP(=S)(OCC)OCC,0.00014577045919371006,0.0029396972764529202,1
+CCOP(=S)(SCSC(C)(C)C)OCC,0.00019068711849574906,0.0034018170593390737,1
+Clc1ccc2c(c1)[n+]([O-])nc(n2)n1cncc1,0.00020190555530632425,0.03094282625719898,1
+ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,0.0002625296750418184,0.0014370243329576793,1
+OC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,0.00027647194701359843,0.01497899097540358,1
+OC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,0.0002764719511333511,0.018693951262222713,1
+ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,0.000328162093802273,0.0009393540815108845,1
+CCOP(=S)(SCSC(C)(C)C)OCC,0.00034670385697674235,0.0034018170593390737,1
+CCSCCSP(=S)(OCC)OCC,0.00036442614798427517,0.0032087834044491653,1
+ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,0.00039379451256272755,0.0009393540815108845,1
+ClC1C2OC2C2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,0.0005137200498000217,0.0006312904946719587,1
+CNC(=O)ON=CC(SC)(C)C,0.0005255875464343458,0.03526134931259953,1
+COC(=O)C(Oc1ccc(cc1)Oc1ccc(cc1Cl)Cl)C,0.0005861906011027885,0.03401894999785191,1
+CCSCSP(=S)(OCC)OCC,0.0006144925475253195,0.0015689457511481922,1
+CCSCSP(=S)(OCC)OCC,0.0006144925612602997,0.0015930132454562913,1
+OC1CCCCCc2cc(O)cc(c2C(=O)OC(CCC1)C)O,0.0006203550142861557,1.1513016524808917,1
+ClC1CC2C(C1Cl)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.000634488903569986,0.0005835576184954016,1
+ClC(C(c1ccc(cc1)Cl)c1ccc(cc1)Cl)(Cl)Cl,0.0007052459522690667,0.043153175434619336,1
+CCSCCSP(=S)(OCC)OCC,0.0008017375255654054,0.0029396972764529202,1
+CCOP(=O)(SC(CC)C)SC(CC)C,0.0008210296720157477,0.012277367120843022,1
+ClC1C=CC2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,0.0008437853335108407,0.0008952202189140214,1
+c1ccc(cc1)[Sn](c1ccccc1)c1ccccc1,0.0008571117562305596,0.3621072701107427,1
+CNC(=O)CSP(=S)(OC)OC,0.000872381733741038,0.011977939066676562,1
+CCS(=O)CCSP(=O)(OC)OC,0.0008932752807580748,0.001392123295168118,1
+CCOP(=O)(SC(CC)C)SC(CC)C,0.0009245829520661433,0.01634116998391423,1
+COP(=S)(Oc1ccc(cc1)N(=O)=O)OC,0.0009498211030948742,0.025384429146594292,1
+OC(=O)c1ccccc1.CCC(C1OC2(C=CC1C)OC1CC=C(C)C(OC3CC(OC)C(C(O3)C)OC3CC(OC)C(C(O3)C)NC)C(C)C=CC=C3C4(C(C(=O)OC(C2)C1)C=C(C)C(C4OC3)O)O)C,0.0009918273033473258,0.0020195689898693642,1
+Clc1c(Cl)c(Cl)c(c(c1Cl)Cl)Cl,0.0010183220720957982,0.09921198034267042,1
+COC1CC(OC2C(C)C=CC=C3COC4C3(O)C(C=C(C4O)C)C(=O)OC3CC(CC=C2C)OC2(C3)C=CC(C(O2)C(C)C)C)OC(C1OC1CC(OC)C(C(O1)C)NC(=O)C)C,0.0011109849279118543,0.0018866442387745258,1
+COP(=O)(SCCS(=O)(=O)CC)OC,0.0011437981092748413,0.0012462691090840692,1
+COC1CC(OC2C(C)C=CC=C3COC4C3(O)C(C=C(C4O)C)C(=O)OC3CC(CC=C2C)OC2(C3)C=CC(C(O2)C(CC)C)C)OC(C1OC1CC(OC)C(C(O1)C)O)C.COC1CC(OC(C1OC1CC(OC)C(C(O1)C)O)C)OC1C(C)C=CC=C2COC3C2(O)C(C=C(C3O)C)C(=O)OC2CC(CC=C1C)OC1(C2)C=CC(C(O1)C(C)C)C,0.0011546496256700967,0.0021478186033654857,1
+O=C1CCCC(=O)C1C(=O)c1ccc(cc1Cl)S(=O)(=O)C,0.0012166633663470796,0.017372736621606367,1
+ClC1CC2C(C1Cl)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.0012201709684038192,0.0005942117597564336,1
+ClC12C(Cl)(Cl)C3(C4(C1(Cl)C1(C2(Cl)C3(C4(C1(Cl)Cl)Cl)Cl)Cl)Cl)Cl,0.0012831252531881078,6.239999085601705e-05,0.625
+CCOP(=O)(N1CCSC1=O)SC(CC)C,0.001341107599716744,0.01204374051145044,1
+COP(=O)(SC)N,0.0013817128392583306,0.33442367385922134,1
+O=C1CCCC(=O)C1C(=O)c1ccc(cc1[N+](=O)[O-])S(=O)(=O)C,0.001414591694222218,0.01480091228679537,1
+CCOP(=S)(Oc1ccc(cc1)N(=O)=O)OCC,0.001442007505168395,0.020357599749119566,1
+CCOP(=S)(Oc1ccccc1C(=O)OC(C)C)NC(C)C,0.0014476216329334154,0.11305876392072264,1
+CCSCCSP(=O)(OC)OC,0.001519854088965729,0.0009444327450089038,1
+CCOc1cc(nc(n1)CC)OP(=S)(OC)OC,0.0015395577035464635,0.008340344661164758,1
+COC(=O)/C=C(/OP(=O)(OC)OC)\C,0.0015614663384413926,0.02855485815997305,1
+COC(=O)C=C(OP(=O)(OC)OC)C,0.001561466365033004,0.019128379067914394,1
+CSc1ccc(cc1C)OP(=S)(OC)OC,0.001616797099077973,0.009022712543242823,1
+COP(=S)(Oc1ccc(c(c1)C)[N+](=O)[O-])OC,0.001659247904766673,0.012452573244964524,1
+C1CCC(CC1)[Sn](n1ncnc1)(C1CCCCC1)C1CCCCC1,0.0018110419025972907,0.013846742083373052,1
+ClC1C2(Cl)C3C4C5C1(Cl)C(C2(Cl)C5C3C1C4O1)(Cl)Cl,0.0018377077252927285,0.00013082348029644925,1
+CNC(=O)CCSCCSP(=O)(OC)OC,0.001879329112916984,0.002248599256352105,1
+N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)[C@H](C(C)C)Nc1ccc(cc1Cl)C(F)(F)F,0.001988416717024977,0.011949513448740262,1
+COP(=S)(Oc1ccc(cc1)[N+](=O)[O-])OC,0.0019946243391447357,0.016643996746722955,1
+CNC(=O)C=C(OP(=O)(OC)OC)C,0.0020164586039868883,0.01114229082863414,1
+COP(=O)(SC)N,0.0020548549325897737,0.33442367385922134,1
+CCCSP(=O)(SCCC)OCC,0.002063225311384027,0.004637753717447314,1
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diff --git a/paper/data/combined-cv.id b/paper/data/combined-cv.id
new file mode 100644
index 0000000..0591b35
--- /dev/null
+++ b/paper/data/combined-cv.id
@@ -0,0 +1 @@
+56c42e262b72ed11e7000001
diff --git a/paper/data/combined-test-predictions.csv b/paper/data/combined-test-predictions.csv
index d378693..976ca41 100644
--- a/paper/data/combined-test-predictions.csv
+++ b/paper/data/combined-test-predictions.csv
@@ -1,151 +1,151 @@
-SMILES,LOAEL,Confidence,Dataset
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+Clc1ccc(c(c1)Cl)C1(OCCO1)Cn1cncn1,0.03798219426521996,0.04563102499286343,1,combined-prediction
+CC(C(c1cncnc1)(c1ccc(cc1)OC(F)(F)F)O)C,0.038746408312020406,0.026593616679335016,1,combined-prediction
+CC(N(C(=O)SCC(=C(Cl)Cl)Cl)C(C)C)C,0.04102878665011248,0.03101114141686048,1,combined-prediction
+COP(=S)(Oc1ccc(c(c1)C)[N+](=O)[O-])OC,0.042491175292669145,0.012534951842686624,1,combined-prediction
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+CN(C(=S)SSC(=S)N(C)C)C,0.04783039657471141,0.052029910797683425,1,combined-prediction
+CN(C(=O)Oc1nc(nc(c1C)C)N(C)C)C,0.05161859628615915,0.03602454025477674,1,combined-prediction
+C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,0.05350296944357954,0.01934170105611924,1,combined-prediction
+COCN(c1c(CC)cccc1CC)C(=O)CCl,0.05375006811431045,0.30333289283418186,1,combined-prediction
+CCOc1ccc2c(c1)C(=CC(N2)(C)C)C,0.05522147585284508,0.20999292062465813,1,combined-prediction
+O=C(c1ccc(cc1S(=O)(=O)C)C(F)(F)F)c1cnoc1C1CC1,0.05566064749641608,0.028785189377791072,1,combined-prediction
+Clc1ccccc1c1nnc(nn1)c1ccccc1Cl,0.05706818876652619,0.08591820939424627,1,combined-prediction
+OC(=O)COc1ccc(cc1C)Cl,0.057322598023636456,0.06409863417711698,1,combined-prediction
+CCOC(=O)NCCOc1ccc(cc1)Oc1ccccc1,0.057576722828150476,0.1896606844312623,1,combined-prediction
+N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C(c1ccc(cc1)Cl)C(C)C,0.05953797389131243,0.016001687478085606,1,combined-prediction
+N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)(C)C,0.06009909138187043,0.027078346141040016,1,combined-prediction
+CC(OC(=O)C(c1ccc(cc1)Br)(c1ccc(cc1)Br)O)C,0.06061453423316249,0.09289647796203974,1,combined-prediction
+Clc1cc(ccc1Oc1ccc(c(c1)C(=O)NS(=O)(=O)C)[N+](=O)[O-])C(F)(F)F,0.06267621846158328,0.07907394396694326,1,combined-prediction
+COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1Cl,0.06288907725176857,0.24059334196784166,1,combined-prediction
+CCCCN(SN(C(=O)Oc1cccc2c1OC(C2)(C)C)C)CCCC,0.06569530810416269,0.029336721918170473,1,combined-prediction
+O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(c(c1F)Cl)F,0.06690994773808298,0.05286631638058653,1,combined-prediction
+CSc1nnc(c(=O)n1N)C(C)(C)C,0.06719929397120725,0.013701160159437665,0.11538461538461539,combined-prediction
+O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(c(c1)Cl)OC(C(OC(F)(F)F)F)(F)F,0.06758613754894155,0.06936283864904705,1,combined-prediction
+CCOc1ccc(cc1)C(COCc1cccc(c1)Oc1ccccc1)(C)C,0.06839527058523667,0.12843709655351573,1,combined-prediction
+C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,0.06904967382858089,0.07255184301853296,1,combined-prediction
+OC(=O)COc1nc(Cl)c(cc1Cl)Cl,0.0721330354641874,0.04327585927626025,1,combined-prediction
+O=C(N(C)C)Nc1cccc(c1)C(F)(F)F,0.07395704796137248,0.057843156201256546,1,combined-prediction
+OC(C(C)(C)C)C(n1cncn1)Oc1ccc(cc1)c1ccccc1,0.07409262028018154,0.1554702671615349,1,combined-prediction
+CCNc1nc(NC(C)C)nc(n1)Cl,0.07789199862212233,0.04120360138919732,1,combined-prediction
+O=C(C1=C(C)OCCS1)Nc1ccccc1,0.08117223892684251,0.21865371953286192,1,combined-prediction
+CNC(=O)Oc1cc(C)c(c(c1)C)SC,0.0827758354922366,0.02137860199550009,1,combined-prediction
+OC(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.08452667530010859,0.07311717098654681,1,combined-prediction
+O=C(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.08510674803234901,0.061223837435072606,1,combined-prediction
+CC(NC(=O)N1CC(=O)N(C1=O)c1cc(Cl)cc(c1)Cl)C,0.08783443947180365,0.07084460220094288,1,combined-prediction
+CCC(=O)Nc1ccc(c(c1)Cl)Cl,0.09170952329114665,0.13348596957039296,1,combined-prediction
+N#CC(c1ccccc1)(Cn1cncn1)CCc1ccc(cc1)Cl,0.09203781459712614,0.049528243300647194,1,combined-prediction
+ClC(=CC1C(C1(C)C)C(=O)OCc1cccc(c1)Oc1ccccc1)Cl,0.09583741068272783,0.05112367301257726,1,combined-prediction
+OC(=O)C(Oc1ccc(cc1Cl)Cl)C,0.0967821447110451,0.01969166251145765,1,combined-prediction
+COP(=O)(NC(=O)C)SC,0.10236623790044716,0.01603420284847195,1,combined-prediction
+O=C1N(c2cc(Cl)cc(c2)Cl)C(=O)C2(C1(C)C2)C,0.11086164698737522,0.08912049056135805,1,combined-prediction
+COC(=O)c1c(nc(c(c1CC(C)C)C1=NCCS1)C(F)(F)F)C(F)F,0.11151045292283465,0.03652048573032131,1,combined-prediction
+Clc1ccc(cc1)CCC(C(C)(C)C)(Cn1cncn1)O,0.11516531274058425,0.05167638607399666,1,combined-prediction
+COC(=O)NC(=S)Nc1ccccc1NC(=S)NC(=O)OC,0.12412602138191925,0.23311870284543604,1,combined-prediction
+CCOc1cc(ccc1[N+](=O)[O-])Oc1ccc(cc1Cl)C(F)(F)F,0.12855945536132327,0.0532083628742947,1,combined-prediction
+CCc1ccc(cc1)C(=O)NN(C(C)(C)C)C(=O)c1cc(C)cc(c1)C,0.1367492600870436,1.000096018241897,1,combined-prediction
+c1scc(n1)c1nc2c([nH]1)cccc2,0.1490700414533971,0.07124290059304189,1,combined-prediction
+CC(N1C(=O)c2ccccc2NS1(=O)=O)C,0.14982590230152565,0.4889107282616925,1,combined-prediction
+N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.15013314047110002,0.014761743547403035,1,combined-prediction
+Cc1cccc2c1n1cnnc1s2,0.1506048130761757,0.3856935237852282,1,combined-prediction
+ClC(SN1C(=O)c2c(C1=O)cccc2)(Cl)Cl,0.15174119992085178,0.24709110501373716,1,combined-prediction
+CCC(Nc1c(cc(cc1[N+](=O)[O-])C(C)(C)C)[N+](=O)[O-])C,0.15338553104123837,0.07663751118571574,1,combined-prediction
+Cc1nc(Nc2ccccc2)nc(c1)C1CC1,0.15801925188118618,0.19454416629038565,1,combined-prediction
+c1ccc(cc1)Nc1ccccc1,0.16546268922726798,0.251186443435064,1,combined-prediction
+Clc1cc(Cl)c(cc1n1nc(n(c1=O)C(F)F)C)NS(=O)(=O)C,0.1730416993562668,0.024541035827570765,1,combined-prediction
+CCCC1COC(O1)(Cn1cncn1)c1ccc(cc1Cl)Cl,0.1767866659490005,0.03831103939366669,1,combined-prediction
+C#CCC1=C(C)C(CC1=O)OC(=O)C1C(C1(C)C)C=C(C)C,0.18559079091504613,0.15407527700390689,1,combined-prediction
+COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1CCC(F)(F)F,0.20017699986539617,0.1291134993044419,1,combined-prediction
+CC(=CC1C(C1(C)C)C(=O)OCc1coc(c1)Cc1ccccc1)C,0.2068313193675311,0.16342863567669358,1,combined-prediction
+CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.21666838084755125,0.07899149299378624,1,combined-prediction
+CC(Nc1nc(NC(C)C)nc(n1)Cl)C,0.2198425631222415,0.057817340618821475,1,combined-prediction
+CNC(=O)Oc1ccccc1OC(C)C,0.23417894234275483,0.05328869177586038,1,combined-prediction
+CCCCCCCCSC(=O)Oc1cc(Cl)nnc1c1ccccc1,0.2408184692696632,0.15669364902191532,1,combined-prediction
+O=C1N(OCC1(C)C)Cc1ccccc1Cl,0.253443853488009,0.07178252950433611,1,combined-prediction
+Cc1ccc(cc1)N(S(=O)(=O)N(C)C)SC(Cl)(Cl)F,0.25917417547047744,0.0672060941474649,1,combined-prediction
+CC1N(C(=O)NC2CCCCC2)C(=O)SC1c1ccc(cc1)Cl,0.2594374890563992,0.04040958911705992,1,combined-prediction
+CCN(C(=O)C(Oc1cccc2c1cccc2)C)CC,0.27189561466298434,0.2470979480063625,1,combined-prediction
+OC(=O)COc1ccc(cc1Cl)Cl,0.28049546719325014,0.04220782045663329,1,combined-prediction
+CCOC(=O)CC(C(=O)OCC)SP(=S)(OC)OC,0.2921073325236663,0.024525489375934365,1,combined-prediction
+CNC(=O)Oc1cccc2c1cccc2,0.2981792578159244,0.1209025875895827,1,combined-prediction
+Clc1ccc(cc1)CN(C(=O)Nc1ccccc1)C1CCCC1,0.31170568268957544,0.06564925552956072,1,combined-prediction
+ClCCOc1ccccc1S(=O)(=O)NC(=O)Nc1nc(C)nc(n1)OC,0.31207588849423984,0.14729599082809905,1,combined-prediction
+CCC(n1c(=O)[nH]c(c(c1=O)Br)C)C,0.3169092998307417,0.2948426093660432,0.25,combined-prediction
+ClC(SN1C(=O)C2C(C1=O)CC=CC2)(Cl)Cl,0.32935301892961466,0.07976114599708196,1,combined-prediction
+CC(=CC1C(C1(C)C)C(=O)OCc1cccc(c1)Oc1ccccc1)C,0.42802021191337764,0.059860013602209265,1,combined-prediction
+CC(Oc1ccccn1)COc1ccc(cc1)Oc1ccccc1,0.43361266621389954,0.13723730603736453,1,combined-prediction
+N#Cc1c[nH]cc1c1cccc2c1OC(O2)(F)F,0.44926154899338216,0.1795731486323763,1,combined-prediction
+Clc1c(Cl)c([N+](=O)[O-])c(c(c1Cl)Cl)Cl,0.47403843842257615,0.0731675127439027,1,combined-prediction
+COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1C(=O)OC,0.4837900188743661,0.12263485736061217,1,combined-prediction
+Nc1nc(NC2CC2)nc(n1)N,0.5144905821145022,0.10982169517930987,1,combined-prediction
+CCCCOCCOCCOCc1cc2OCOc2cc1CCC,0.5170806512852409,0.3350153436602428,1,combined-prediction
+COCC(N(c1c(C)cccc1CC)C(=O)CCl)C,0.5285529966699751,0.08251097640354867,1,combined-prediction
+O=Cc1ccco1,0.624453213155231,0.12486833177320306,0.1111111111111111,combined-prediction
+[O-][N+](=O)c1cc(Cl)c(c(c1)Cl)N,0.7245881151318344,0.15109322689681717,1,combined-prediction
+COC(=O)c1c(Cl)c(Cl)c(c(c1Cl)Cl)C(=O)OC,0.7681550277825105,0.747753445270089,1,combined-prediction
+COC(=O)NS(=O)(=O)c1ccc(cc1)N,0.7817895162025876,0.49783922754030796,1,combined-prediction
+OC(=O)c1nc(Cl)c(c(c1Cl)N)Cl,0.8282972172278201,0.4720460499425296,1,combined-prediction
+CC(C1(C)N=C(NC1=O)c1nc2ccccc2cc1C(=O)O)C,0.8351128195663594,0.8106254748473309,1,combined-prediction
+COC(=O)Nc1nc2c([nH]1)cccc2,0.8499546987221808,0.08145337691491894,1,combined-prediction
+CC(Oc1cccc(c1)NC(=O)c1ccccc1C(F)(F)F)C,0.9077984526598573,0.1053554610733538,1,combined-prediction
+O=C(C1(C)CCCCC1)Nc1ccc(c(c1Cl)Cl)O,0.9662594125910484,0.4231253966583784,1,combined-prediction
+ClCCP(=O)(O)O,0.9723587138566308,2.424380344082731,1,combined-prediction
+COc1cccc(c1C)C(=O)NN(C(C)(C)C)C(=O)c1cc(C)cc(c1)C,1.1154252951100516,0.2488946852404996,1,combined-prediction
+CCC(c1noc(c1)NC(=O)c1c(OC)cccc1OC)(CC)C,1.5854670852219546,0.43069366532085357,1,combined-prediction
+OC(=O)COc1nc(F)c(c(c1Cl)N)Cl,1.9605490478397496,0.13193009603279973,1,combined-prediction
+CC(OC(=O)Nc1cccc(c1)Cl)C,2.340158076742021,0.10044437465404735,1,combined-prediction
+Oc1ccccc1c1ccccc1,3.119727015073393,0.20273166023828124,1,combined-prediction
+OC(=O)CNCP(=O)(O)O,5.559726007239,0.700841565636653,0.16666666666666666,combined-prediction
diff --git a/paper/data/combined-test-predictions.id b/paper/data/combined-test-predictions.id
new file mode 100644
index 0000000..27835d9
--- /dev/null
+++ b/paper/data/combined-test-predictions.id
@@ -0,0 +1 @@
+56c42aa82b72ed1054000004
diff --git a/paper/data/common-median.csv b/paper/data/common-median.csv
index f1244b0..e83961d 100644
--- a/paper/data/common-median.csv
+++ b/paper/data/common-median.csv
@@ -1,24 +1,24 @@
 SMILES,mazzatorta,swiss
 N#Cc1nn(c(c1S(=O)C(F)(F)F)N)c1c(Cl)cc(cc1Cl)C(F)(F)F,0.0001372533562906347,0.00013496580117055152
+CCOP(=S)(SCSC(C)(C)C)OCC,0.0002080223110862717,0.00034670385697674235
 OC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,0.00027647194701359843,0.0002764719511333511
 CCSCSP(=S)(OCC)OCC,0.0006144925612602997,0.0006144925475253195
 CCOP(=O)(SC(CC)C)SC(CC)C,0.0009245829520661433,0.0008210296720157477
 COP(=S)(Oc1ccc(c(c1)C)[N+](=O)[O-])OC,0.001659247904766673,0.08332310268057162
-CCOP(=S)(SCSC(C)(C)C)OCC,0.002438483757733518,0.00034670385697674235
-COP(=O)(SC)N,0.003046853953236319,0.0020548549325897737
+COP(=O)(SC)N,0.002054854991717517,0.0020548549325897737
+CSc1ccc(cc1C)OP(=S)(OC)OC,0.0025868753585247565,0.0016527259802523342
 CCOP(=S)(Oc1ccc2c(c1)oc(=O)c(c2C)Cl)OCC,0.003445751195813495,0.0033630532459809582
 CCOP(=S)(Oc1ncn(n1)c1ccccc1)OCC,0.004149212048673449,0.004149211896481245
-COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.00467202701142753,0.0039030031199302137
+COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.004713372383210075,0.0039030031199302137
 CCOP(=S)(OCC)SCSP(=S)(OCC)OCC,0.005201883810203027,0.004681695305160139
 CCOP(=O)(Oc1ccc(c(c1)C)SC)NC(C)C,0.005274306112287868,0.0035601567181414275
 CCOP(=S)(SCn1c(=O)oc2c1ccc(c2)Cl)OCC,0.0054376113486863924,0.0636200517424888
-COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,0.005451835179110433,0.008508644649457775
 CC(Cc1ccc(cc1)C(C)(C)C)CN1CC(C)OC(C1)C,0.005601647965290344,0.005601648122412352
-CSc1ccc(cc1C)OP(=S)(OC)OC,0.005892327205528613,0.0016527259802523342
 Fc1ccc(cc1)[Si](c1ccc(cc1)F)(Cn1cncn1)C,0.006341300659739408,0.023779877474022784
 COC(=O)N(C(=O)N1COC2(C(=N1)c1ccc(cc1C2)Cl)C(=O)OC)c1ccc(cc1)OC(F)(F)F,0.006820319755914397,0.006820319575237628
-Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,0.00694452873492003,0.01932390597300771
-O=S1OCC2C(CO1)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.007126617932723449,0.015481963173347177
+Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,0.00694452873492003,0.00920904883059355
+COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,0.0070905370826580775,0.008508644649457775
+O=S1OCC2C(CO1)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.007126617932723449,0.01228727229779905
 CCCCOC(=O)C(Oc1ccc(cc1)Oc1ccc(cn1)C(F)(F)F)C,0.007825509706097071,0.009886227162529472
 COP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OC,0.009301369775521361,0.003100456591840454
 CCOC(=O)C(Oc1ccc(cc1)Oc1cnc2c(n1)ccc(c2)Cl)C,0.009924832004782804,0.04157699893895499
@@ -31,15 +31,15 @@ CC(Oc1cc(c(cc1Cl)Cl)n1nc(oc1=O)C(C)(C)C)C,0.01448347496337274,0.0104281016973780
 N#Cc1c(Cl)cccc1Cl,0.014533918736325764,0.0186034162597095
 N#Cc1c(Cl)c(C#N)c(c(c1Cl)Cl)Cl,0.015042627044387032,0.08141821878808377
 ClCC1CN(C(=O)C1Cl)c1cccc(c1)C(F)(F)F,0.016019730669239306,0.05030195369030707
+ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,0.016160652565775233,0.01616065190994549
 CCCN(C(=O)n1cncc1)CCOc1c(Cl)cc(cc1Cl)Cl,0.01672571818640967,0.05707983190600125
 BrC1COC(C1)(Cn1cncn1)c1ccc(cc1Cl)Cl,0.017185416964361586,0.017185417014945824
-ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,0.01808617712680377,0.01616065190994549
 OC(=O)C(CCP(=O)(O)C)N,0.019323475195614302,1.2637552440957067
 CCN(C(=O)SCc1ccc(cc1)Cl)CC,0.019396419126203733,0.016680921188449865
 OC(=O)COc1ccc(cc1C)Cl,0.019938294964743114,0.0947069010825298
-N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.021874904009467275,0.04835505096829608
+N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.022450559378137468,0.04835505096829608
 CNC(=O)ON=C(C(=O)N(C)C)SC,0.02280382932847922,0.02072868120754643
-O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1)Cl,0.02446221194980985,0.4023390123323988
+O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1)Cl,0.02510595436954169,0.4023390123323988
 O=C(N(C)C)Nc1ccc(c(c1)Cl)Cl,0.026276896280264014,0.007293179580314936
 CCN(c1nc(cc(n1)C)OP(=S)(OC)OC)CC,0.026692119922880408,0.0068777238395693234
 OC(C(Cl)(Cl)Cl)(c1ccc(cc1)Cl)c1ccc(cc1)Cl,0.030365547751564796,0.005938151689011985
@@ -51,10 +51,10 @@ OC(=O)C(Oc1ccc(cc1Cl)Cl)C,0.03828744186371015,0.15527684755838006
 CC(C(c1cncnc1)(c1ccc(cc1)OC(F)(F)F)O)C,0.038746408312020406,0.08469772512288609
 CC(N(C(=O)SCC(=C(Cl)Cl)Cl)C(C)C)C,0.04102878665011248,0.03544887229174679
 CNC(=O)Oc1cc(C)c(c(c1)C)SC,0.041276958181115306,0.1242747128033579
-CN(C(=S)SSC(=S)N(C)C)C,0.04432283415923257,0.03036190470594063
 O=C1OC(C(=O)N1Nc1ccccc1)(C)c1ccc(cc1)Oc1ccccc1,0.044873074905021335,0.045407278177700156
 COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,0.04569504751402555,0.009139009427670286
 CC1=C(C)S(=O)(=O)CCS1(=O)=O,0.047557630336441704,0.23778815168220852
+CN(C(=S)SSC(=S)N(C)C)C,0.04783039657471141,0.03036190470594063
 CON(C(=O)Nc1ccc(cc1)Br)C,0.048243951057630914,0.014357399945172603
 Clc1ccc(cc1)CCC(C(C)(C)C)(Cn1cncn1)O,0.05165383561566402,0.17867678986550448
 Cc1ccc(cc1)N(S(=O)(=O)N(C)C)SC(Cl)(Cl)F,0.051834835094095484,0.27357274077439286
@@ -80,7 +80,7 @@ N#CC(c1ccccc1)(Cn1cncn1)CCc1ccc(cc1)Cl,0.10391366164191661,0.09203781459712614
 CCOc1cc(ccc1[N+](=O)[O-])Oc1ccc(cc1Cl)C(F)(F)F,0.11058877880543937,0.14653013191720715
 COC(=O)c1c(nc(c(c1CC(C)C)C1=NCCS1)C(F)(F)F)C(F)F,0.11151045196043953,0.11151045388522976
 Clc1cc(ccc1Oc1ccc(c(c1)C(=O)NS(=O)(=O)C)[N+](=O)[O-])C(F)(F)F,0.11395676083924232,0.011395676083924233
-CCNc1nc(NC(C)C)nc(n1)Cl,0.11591071091933607,0.056718974985359355
+CCNc1nc(NC(C)C)nc(n1)Cl,0.11591071091933607,0.04636428436773443
 O=C(C1=C(C)OCCS1)Nc1ccccc1,0.1274956638724717,0.034848813981213346
 C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,0.13459866849613178,0.05350296944357954
 CCc1ccc(cc1)C(=O)NN(C(C)(C)C)C(=O)c1cc(C)cc(c1)C,0.13618183361575933,0.13731668655832788
@@ -90,11 +90,12 @@ CCC(c1noc(c1)NC(=O)c1c(OC)cccc1OC)(CC)C,0.15252975563710267,1.6952764753748983
 CC(N1C(=O)c2ccccc2NS1(=O)=O)C,0.1560686482307559,0.14982590230152565
 Cc1nc(Nc2ccccc2)nc(c1)C1CC1,0.15801925526767843,0.15801924849469393
 Cc1cccc2c1n1cnnc1s2,0.16381576159162972,0.1373938645607217
+ClC(SN1C(=O)c2c(C1=O)cccc2)(Cl)Cl,0.16860133324539087,0.08430066662269543
 CCC(Nc1c(cc(cc1[N+](=O)[O-])C(C)(C)C)[N+](=O)[O-])C,0.16929970598735858,0.13747135609511818
-C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,0.1697708869122168,0.061250674376451514
+C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,0.1697708869122168,0.06904967382858089
 CCCCCCCCSC(=O)Oc1cc(Cl)nnc1c1ccccc1,0.17813968959673715,0.3034972489425892
 CN1CN(C)CSC1=S,0.18486987933542975,0.027422365728598172
-CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.18534506246313948,0.22661523159035935
+CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.18534506246313948,0.24799169923196304
 CNC(=O)Oc1cccc2c1cccc2,0.1878529324240324,0.2991731924668564
 COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1CCC(F)(F)F,0.19051986050321804,0.2098341392275743
 CC(Nc1nc(NC(C)C)nc(n1)Cl)C,0.21766590408142725,0.22201922216305578
@@ -114,8 +115,7 @@ CC1N(C(=O)NC2CCCCC2)C(=O)SC1c1ccc(cc1)Cl,0.4534134152107278,0.06546156290207059
 ClCCOc1ccccc1S(=O)(=O)NC(=O)Nc1nc(C)nc(n1)OC,0.5494924735209582,0.07465930346752149
 CCOC(=O)CC(C(=O)OCC)SP(=S)(OC)OC,0.6205388929259232,0.2603236331298995
 COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1C(=O)OC,0.655542030995076,0.31203800675365617
-ClC(SN1C(=O)c2c(C1=O)cccc2)(Cl)Cl,0.6631652440985374,0.08430066662269543
 [O-][N+](=O)c1cc(Cl)c(c(c1)Cl)N,0.9419645496713847,0.5651787298028309
-ClCCP(=O)(O)O,1.4025957248513201,0.9066120392542251
+ClCCP(=O)(O)O,1.0381053884590363,0.9066120392542251
 CC(OC(=O)Nc1cccc(c1)Cl)C,3.510237115113031,0.14040948460452124
-OC(=O)CNCP(=O)(O)O,5.914602135360638,5.350743398456257
+OC(=O)CNCP(=O)(O)O,5.914602135360638,5.559726007239
diff --git a/paper/functional-groups-reduced.csv b/paper/data/functional-groups-reduced.csv
index 525acbf..525acbf 100644
--- a/paper/functional-groups-reduced.csv
+++ b/paper/data/functional-groups-reduced.csv
diff --git a/paper/functional-groups-reduced4R.csv b/paper/data/functional-groups-reduced4R.csv
index e37c41d..e37c41d 100644
--- a/paper/functional-groups-reduced4R.csv
+++ b/paper/data/functional-groups-reduced4R.csv
diff --git a/paper/functional-groups.csv b/paper/data/functional-groups.csv
index 0e2b4ca..0e2b4ca 100644
--- a/paper/functional-groups.csv
+++ b/paper/data/functional-groups.csv
diff --git a/paper/data/mazzatorta-cv.csv b/paper/data/mazzatorta-cv.csv
new file mode 100644
index 0000000..200058c
--- /dev/null
+++ b/paper/data/mazzatorta-cv.csv
@@ -0,0 +1,519 @@
+SMILES,LOAEL_measured_median,LOAEL_predicted,Confidence
+ClC12C3C4(C(C1(Cl)Cl)(C1(C2(C3(Cl)C(C41Cl)(Cl)Cl)Cl)Cl)Cl)Cl,1.9565721591442926e-05,0.0014218133641616987,0.625
+ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C=C2)(Cl)Cl)Cl,2.7404023436797774e-05,0.0012794179116857743,1
+ClC1C2OC2C2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,6.421500622500271e-05,0.0006312904946719587,1
+ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,0.0001312648375209092,0.0009393540815108845,1
+N#Cc1nn(c(c1S(=O)C(F)(F)F)N)c1c(Cl)cc(cc1Cl)C(F)(F)F,0.0001372533562906347,0.030320302552666413,1
+CCSCCSP(=S)(OCC)OCC,0.00014577045919371006,0.002904296720614731,1
+CCOP(=S)(SCSC(C)(C)C)OCC,0.0001733519259052264,0.004705691894690802,1
+CCOP(=S)(SCSC(C)(C)C)OCC,0.0002080223110862717,0.0031198946651464694,1
+ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,0.0002625296750418184,0.0009393540815108845,1
+OC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,0.00027647194701359843,0.016349996939185575,1
+ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,0.000328162093802273,0.00023577843419090474,1
+CCSCCSP(=S)(OCC)OCC,0.00036442614798427517,0.002759857629239433,1
+ClC1C2OC2C2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,0.0005137200498000217,0.0008261557263644955,1
+CNC(=O)ON=CC(SC)(C)C,0.0005255875464343458,0.0394300510866583,1
+ClC1CC2C(C1Cl)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.0006100854842019096,0.0005942117597564336,1
+CCSCSP(=S)(OCC)OCC,0.0006144925612602997,0.0013675281812878628,1
+OC1CCCCCc2cc(O)cc(c2C(=O)OC(CCC1)C)O,0.0006203550142861557,0.7144075963289822,1
+ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,0.000656324187604546,0.0009393540815108845,1
+ClC1CC2C(C1Cl)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.0006588923229380624,0.0008687145669203877,1
+ClC1C=CC2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,0.0006696708996117783,0.0008952202189140214,1
+ClC(C(c1ccc(cc1)Cl)c1ccc(cc1)Cl)(Cl)Cl,0.0007052459522690667,0.04147328692582911,1
+COP(=O)(SC)N,0.000708570686799144,0.33442367385922134,1
+CCSCCSP(=S)(OCC)OCC,0.0008017375255654054,0.0029306716037033644,1
+c1ccc(cc1)[Sn](c1ccccc1)c1ccccc1,0.0008571117562305596,0.6096639268673245,1
+CCOP(=O)(SC(CC)C)SC(CC)C,0.0009245829520661433,0.0119018935050396,1
+COP(=S)(Oc1ccc(cc1)N(=O)=O)OC,0.0009498211030948742,0.03193259568430547,1
+ClC1C=CC2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,0.001017899767409903,0.0008952202189140214,1
+Clc1c(Cl)c(Cl)c(c(c1Cl)Cl)Cl,0.0010183220720957982,0.09587934918066965,1
+CNC(=O)CSP(=S)(OC)OC,0.001090477150926923,0.011977939066676569,1
+COC1CC(OC2C(C)C=CC=C3COC4C3(O)C(C=C(C4O)C)C(=O)OC3CC(CC=C2C)OC2(C3)C=CC(C(O2)C(C)C)C)OC(C1OC1CC(OC)C(C(O1)C)NC(=O)C)C,0.0011109849279118543,0.030023844212028958,1
+COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,0.0011344859332252924,0.02189964718237587,1
+ClC1CC2C(C1Cl)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.0012201709684038192,0.0005942117597564336,1
+ClC12C(Cl)(Cl)C3(C4(C1(Cl)C1(C2(Cl)C3(C4(C1(Cl)Cl)Cl)Cl)Cl)Cl)Cl,0.0012831252531881078,6.239999085601705e-05,0.625
+CCOP(=S)(Oc1ccc(cc1)N(=O)=O)OCC,0.001442007505168395,0.02324515370361023,1
+CCOP(=S)(Oc1ccccc1C(=O)OC(C)C)NC(C)C,0.0014476216329334154,0.10541114923752212,1
+CCOc1cc(nc(n1)CC)OP(=S)(OC)OC,0.0015395577035464635,0.008933340243284798,1
+COC(=O)C=C(OP(=O)(OC)OC)C,0.001561466365033004,0.05630681195605883,1
+CSc1ccc(cc1C)OP(=S)(OC)OC,0.001616797099077973,0.00998805136771544,1
+COP(=S)(Oc1ccc(c(c1)C)[N+](=O)[O-])OC,0.001659247904766673,0.015728201435628045,1
+ClC1C2(Cl)C3C4C5C1(Cl)C(C2(Cl)C5C3C1C4O1)(Cl)Cl,0.0018377077252927285,0.00013082348029644925,1
+CNC(=O)CCSCCSP(=O)(OC)OC,0.001879329112916984,0.004845236789812529,1
+CNC(=O)C=C(OP(=O)(OC)OC)C,0.0020164586039868883,0.010849425248724263,1
+COP(=O)(SC)N,0.002054854991717517,0.33442367385922134,1
+CCOP(=S)(Oc1ccc2c(c1)oc(=O)c(c2C)Cl)OCC,0.0022052807653206367,0.010561978243550603,1
+S=C1NCCN1,0.0022514113902230405,6.303842268414008,0.14285714285714285
+CO[C@H]1C[C@H](O[C@H]2[C@@H](C)C=CC=C3CO[C@H]4[C@]3(O)[C@@H](C=C([C@H]4O)C)C(=O)O[C@H]3C[C@@H](CC=C2C)O[C@]2(C3)C=C[C@@H]([C@H](O2)[C@H](CC)C)C)O[C@H]([C@@H]1O[C@H]1C[C@H](OC)[C@H]([C@@H](O1)C)O)C,0.002290749011702154,0.0017566214091597164,0.11827956989247312
+S=C1NCCN1,0.0024471862937206963,6.303842268414008,0.14285714285714285
+COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.002646103794082849,0.013342851700514885,1
+COC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,0.0026615073878255148,0.0012344748927784325,1
+COC1CC(OC2C(C)C=CC=C3COC4C3(O)C(C=C(C4O)C)C(=O)OC3CC(CC=C2C)OC2(C3)C=CC(C(O2)C(C)C)C)OC(C1OC1CC(OC)C(C(O1)C)NC(=O)C)C,0.0027774623197796356,0.04161465926335706,0.1348314606741573
+CCOP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OCC,0.002852364738724816,0.012560097152432495,1
+CCOP(=S)(OCC)SCSc1ccc(cc1)Cl,0.0029165972759564764,0.007657588282036417,1
+C1CCN2C(C1)C1CCCCN1CC2,0.002933359023382885,0.16703078340793057,1
+C1CCN2C(C1)C1CCCCN1CC2,0.002984821462389602,0.16703078340793057,1
+CCCCSP(=O)(SCCCC)SCCCC,0.003974424546249488,0.1504496631045886,1
+COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.004134537178254452,0.011073447351926287,1
+CCOP(=S)(Oc1ncn(n1)c1ccccc1)OCC,0.004149212048673449,0.020215395668456124,1
+CCOP(=O)(OC(=CCl)c1ccc(cc1Cl)Cl)OCC,0.004171650398342553,0.04995900954278815,1
+Clc1nc(nc(n1)Cl)Nc1ccccc1Cl,0.004173898399328111,0.1093606955215401,1
+Clc1cccc(n1)C(Cl)(Cl)Cl,0.00433075312836283,0.0664943030028045,0.13043478260869565
+N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C(C(Br)(Br)Br)Br,0.004511229623452476,0.0348345939452587,1
+CCOP(=S)(Oc1ccc2c(c1)oc(=O)c(c2C)Cl)OCC,0.004686221626306353,0.010714479147398627,1
+CCOP(=S)(Oc1cc(C)nc(n1)C(C)C)OCC,0.004928609097226672,0.010980300528105117,1
+CCOP(=O)(Oc1ccc(c(c1)C)SC)NC(C)C,0.004944661980269876,0.008165508970666315,1
+N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Br)Br,0.004948543461552866,0.03481040749532821,1
+N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C(C(C)C)Nc1ccc(cc1Cl)C(F)(F)F,0.004971041792562443,0.011146277874077666,1
+CCN(C(=O)C(=C(OP(=O)(OC)OC)C)Cl)CC,0.005005200069191886,0.006725562763289336,1
+CCNc1nc(nc(n1)Cl)NC(C#N)(C)C,0.005193343612552968,0.03818729902070168,1
+CCOP(=S)(OCC)SCSP(=S)(OCC)OCC,0.005201883810203027,0.001847030797857757,1
+COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.005292207588165698,0.011073447351926287,1
+CCOP(=S)(SCn1c(=O)oc2c1ccc(c2)Cl)OCC,0.0054376113486863924,0.0072971278933458734,1
+CC(Cc1ccc(cc1)C(C)(C)C)CN1CC(C)OC(C1)C,0.005601647965290344,0.24428343783187767,1
+CCOP(=O)(Oc1ccc(c(c1)C)SC)NC(C)C,0.005603950244305859,0.008071940830053378,1
+Fc1ccc(cc1)[Si](c1ccc(cc1)F)(Cn1cncn1)C,0.006341300659739408,0.08859636009379407,1
+COC(=O)Nc1nc2c([nH]1)cc(cc2)S(=O)c1ccccc1,0.006342219438128827,0.3094692255753645,1
+ClCC(N1C(=O)c2c(C1=O)cccc2)SP(=S)(OCC)OCC,0.006347661308292605,0.01655335793163268,1
+COP(=O)(SC)N,0.006377136181192296,0.045296304153967855,0.13333333333333333
+CCP(=S)(Sc1ccccc1)OCC,0.006414179135682054,0.003722191492864322,1
+COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.006615259485207122,0.011073447351926287,1
+OC(C(Cl)(Cl)Cl)(c1ccc(cc1)Cl)c1ccc(cc1)Cl,0.006747899500347733,0.049047172630179624,1
+CNC(=O)Oc1cc(C)c(c(c1)C)N(C)C,0.0067481385934503825,0.07806475331674674,1
+COC(=O)N(C(=O)N1COC2(C(=N1)c1ccc(cc1C2)Cl)C(=O)OC)c1ccc(cc1)OC(F)(F)F,0.006820319755914397,0.05460021012041988,1
+CCOP(=S)(SCSC(C)(C)C)OCC,0.006934077036209056,0.0029602477287608174,1
+Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,0.00694452873492003,0.05356866802000862,1
+COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,0.0070905370826580775,0.018322295358547312,1
+O=S1OCC2C(CO1)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.007126617932723449,0.00036386924695734017,1
+Cc1nn(c(c1C=NOCc1ccc(cc1)C(=O)OC(C)(C)C)Oc1ccccc1)C,0.0073074288460468996,0.48191774605001353,1
+Fc1ccc(cc1)[Si](c1ccc(cc1)F)Cn1cncn1,0.007657523838454347,0.08859636009379407,1
+CCCCOC(=O)C(Oc1ccc(cc1)Oc1ccc(cn1)C(F)(F)F)C,0.007825509706097071,0.01255029551403124,1
+Fc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,0.007943029289634557,0.014438054300148512,1
+CSc1ccc(cc1C)OP(=S)(OC)OC,0.008030092258753932,0.011338986062901048,1
+COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,0.00813048252144793,0.021687531960393556,1
+CCN(c1nc(cc(n1)C)OP(=S)(OC)OC)CC,0.008187766847509327,0.017844597124334886,1
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+[O-][N+](=O)c1cc([N+](=O)[O-])c(c(c1)[N+](=O)[O-])C,0.008805487227420639,0.16464376372059966,1
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diff --git a/paper/data/mazzatorta-cv.id b/paper/data/mazzatorta-cv.id
new file mode 100644
index 0000000..516d987
--- /dev/null
+++ b/paper/data/mazzatorta-cv.id
@@ -0,0 +1 @@
+56c42aab2b72ed10be000001
diff --git a/paper/data/mazzatorta-test-predictions.csv b/paper/data/mazzatorta-test-predictions.csv
index 6c61db5..e508e33 100644
--- a/paper/data/mazzatorta-test-predictions.csv
+++ b/paper/data/mazzatorta-test-predictions.csv
@@ -1,149 +1,149 @@
-SMILES,LOAEL,Confidence,Dataset
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-N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Br)Br,0.03520125762569351,1,mazzatorta-prediction
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diff --git a/paper/data/mazzatorta-test-predictions.id b/paper/data/mazzatorta-test-predictions.id
new file mode 100644
index 0000000..65e6336
--- /dev/null
+++ b/paper/data/mazzatorta-test-predictions.id
@@ -0,0 +1 @@
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diff --git a/paper/data/mazzatorta.csv~ b/paper/data/mazzatorta.csv~
deleted file mode 100644
index 921a53b..0000000
--- a/paper/data/mazzatorta.csv~
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diff --git a/paper/data/median-correlation.csv b/paper/data/median-correlation.csv
index 889453d..e83961d 100644
--- a/paper/data/median-correlation.csv
+++ b/paper/data/median-correlation.csv
@@ -1,121 +1,121 @@
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-CNC(=O)ON=C(C(=O)N(C)C)SC,0.02280382932847922,0.02072868120754643
-COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,0.04569504751402555,0.009139009427670286
-OC(=O)c1nc(Cl)c(c(c1Cl)N)Cl,0.24848916516834604,0.9318343693812976
-CCN(c1nc(cc(n1)C)OP(=S)(OC)OC)CC,0.026692119922880408,0.0068777238395693234
-CCC(=O)Nc1ccc(c(c1)Cl)Cl,0.22010285589875195,0.041269285481015994
-CC1N(C(=O)NC2CCCCC2)C(=O)SC1c1ccc(cc1)Cl,0.4534134152107278,0.06546156290207059
-CCN(C(=O)SCc1ccc(cc1)Cl)CC,0.019396419126203733,0.016680921188449865
-CN(C(=S)SSC(=S)N(C)C)C,0.04432283415923257,0.03036190470594063
-C=CC1(C)OC(=O)N(C1=O)c1cc(Cl)cc(c1)Cl,0.25479642918707424,0.0186990521567307
-COP(=O)(SC)N,0.003046853953236319,0.0020548549325897737
 CCOP(=S)(Oc1cc(C)nc(n1)C(C)C)OCC,0.011992948803251567,0.01642869699075557
-OC(=O)CNCP(=O)(O)O,5.914602135360638,5.350743398456257
-CON(C(=O)Nc1ccc(c(c1)Cl)Cl)C,0.010036375840596658,0.12545469800745823
-CC(OC(=O)Nc1cccc(c1)Cl)C,3.510237115113031,0.14040948460452124
-OC(=O)C(Oc1ccc(cc1Cl)Cl)C,0.03828744186371015,0.15527684755838006
-N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.021874904009467275,0.04835505096829608
+CC(Oc1cc(c(cc1Cl)Cl)n1nc(oc1=O)C(C)(C)C)C,0.01448347496337274,0.010428101697378017
+N#Cc1c(Cl)cccc1Cl,0.014533918736325764,0.0186034162597095
 N#Cc1c(Cl)c(C#N)c(c(c1Cl)Cl)Cl,0.015042627044387032,0.08141821878808377
-CC(C1(C)N=C(NC1=O)c1nc2ccccc2cc1C(=O)O)C,0.06423944765895072,1.605986191473768
-CC(Nc1nc(NC(C)C)nc(n1)Cl)C,0.21766590408142725,0.22201922216305578
-CCOP(=S)(SCSC(C)(C)C)OCC,0.002438483757733518,0.00034670385697674235
-CCOP(=O)(SC(CC)C)SC(CC)C,0.0009245829520661433,0.0008210296720157477
 ClCC1CN(C(=O)C1Cl)c1cccc(c1)C(F)(F)F,0.016019730669239306,0.05030195369030707
-O=C1N(OCC1(C)C)Cc1ccccc1Cl,0.08969617860069455,0.41719152837532353
-CCC(n1c(=O)[nH]c(c(c1=O)Br)C)C,0.23935747721355113,0.39446112244793224
-CC(N(C(=O)SCC(=C(Cl)Cl)Cl)C(C)C)C,0.04102878665011248,0.03544887229174679
-COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1CCC(F)(F)F,0.19051986050321804,0.2098341392275743
-CC(N1C(=O)c2ccccc2NS1(=O)=O)C,0.1560686482307559,0.14982590230152565
+ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,0.016160652565775233,0.01616065190994549
+CCCN(C(=O)n1cncc1)CCOc1c(Cl)cc(cc1Cl)Cl,0.01672571818640967,0.05707983190600125
+BrC1COC(C1)(Cn1cncn1)c1ccc(cc1Cl)Cl,0.017185416964361586,0.017185417014945824
+OC(=O)C(CCP(=O)(O)C)N,0.019323475195614302,1.2637552440957067
+CCN(C(=O)SCc1ccc(cc1)Cl)CC,0.019396419126203733,0.016680921188449865
+OC(=O)COc1ccc(cc1C)Cl,0.019938294964743114,0.0947069010825298
+N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.022450559378137468,0.04835505096829608
+CNC(=O)ON=C(C(=O)N(C)C)SC,0.02280382932847922,0.02072868120754643
+O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1)Cl,0.02510595436954169,0.4023390123323988
+O=C(N(C)C)Nc1ccc(c(c1)Cl)Cl,0.026276896280264014,0.007293179580314936
+CCN(c1nc(cc(n1)C)OP(=S)(OC)OC)CC,0.026692119922880408,0.0068777238395693234
+OC(C(Cl)(Cl)Cl)(c1ccc(cc1)Cl)c1ccc(cc1)Cl,0.030365547751564796,0.005938151689011985
+CCOC(=O)NCCOc1ccc(cc1)Oc1ccccc1,0.03318543029523152,0.08196801536106943
 Clc1ccc(c(c1)Cl)C1(OCCO1)Cn1cncn1,0.03331771398901528,0.042646674541424644
-OC(=O)COc1nc(Cl)c(cc1Cl)Cl,0.1403669879303106,0.0038990829980641837
 CCOCn1c(c2ccc(cc2)Cl)c(c(c1C(F)(F)F)Br)C#N,0.03336499327732185,0.036799624938222635
+CCCCC(c1ccc(cc1)Cl)(Cn1cncn1)C#N,0.03400568081866287,0.20067507097305953
+OC(=O)C(Oc1ccc(cc1Cl)Cl)C,0.03828744186371015,0.15527684755838006
+CC(C(c1cncnc1)(c1ccc(cc1)OC(F)(F)F)O)C,0.038746408312020406,0.08469772512288609
+CC(N(C(=O)SCC(=C(Cl)Cl)Cl)C(C)C)C,0.04102878665011248,0.03544887229174679
+CNC(=O)Oc1cc(C)c(c(c1)C)SC,0.041276958181115306,0.1242747128033579
+O=C1OC(C(=O)N1Nc1ccccc1)(C)c1ccc(cc1)Oc1ccccc1,0.044873074905021335,0.045407278177700156
+COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,0.04569504751402555,0.009139009427670286
+CC1=C(C)S(=O)(=O)CCS1(=O)=O,0.047557630336441704,0.23778815168220852
+CN(C(=S)SSC(=S)N(C)C)C,0.04783039657471141,0.03036190470594063
+CON(C(=O)Nc1ccc(cc1)Br)C,0.048243951057630914,0.014357399945172603
+Clc1ccc(cc1)CCC(C(C)(C)C)(Cn1cncn1)O,0.05165383561566402,0.17867678986550448
+Cc1ccc(cc1)N(S(=O)(=O)N(C)C)SC(Cl)(Cl)F,0.051834835094095484,0.27357274077439286
+CN(C(=O)Oc1nc(nc(c1C)C)N(C)C)C,0.05203825886364726,0.05161859628615915
 O=C1N(c2cc(Cl)cc(c2)Cl)C(=O)C2(C1(C)C2)C,0.05279126047017867,0.16893203350457175
-COP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OC,0.009301369775521361,0.003100456591840454
-COC(=O)c1c(nc(c(c1CC(C)C)C1=NCCS1)C(F)(F)F)C(F)F,0.11151045196043953,0.11151045388522976
-CC(=CC1C(C1(C)C)C(=O)OCc1coc(c1)Cc1ccccc1)C,0.3693416417277341,0.04432099700732809
-Cc1cccc2c1n1cnnc1s2,0.16381576159162972,0.1373938645607217
-CSc1ccc(cc1C)OP(=S)(OC)OC,0.005892327205528613,0.0016527259802523342
-Clc1c(Cl)c([N+](=O)[O-])c(c(c1Cl)Cl)Cl,0.44864352207850955,0.4774244272684517
-ClCCOc1ccccc1S(=O)(=O)NC(=O)Nc1nc(C)nc(n1)OC,0.5494924735209582,0.07465930346752149
-C#CCC1=C(C)C(CC1=O)OC(=O)C1C(C1(C)C)C=C(C)C,0.316253365684832,0.05492821614526029
-BrC1COC(C1)(Cn1cncn1)c1ccc(cc1Cl)Cl,0.017185416964361586,0.017185417014945824
-O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(c(c1F)Cl)F,0.06690994773808298,0.19325167158375256
-N#Cc1c(Cl)cccc1Cl,0.014533918736325764,0.0186034162597095
-CC(NC(=O)N1CC(=O)N(C1=O)c1cc(Cl)cc(c1)Cl)C,0.13932359364492994,0.03634528529867737
-O=C(N(C)C)Nc1cccc(c1)C(F)(F)F,0.06459882942614491,0.10157735340683115
-CCCN(C(=O)n1cncc1)CCOc1c(Cl)cc(cc1Cl)Cl,0.01672571818640967,0.05707983190600125
-N#Cc1nn(c(c1S(=O)C(F)(F)F)N)c1c(Cl)cc(cc1Cl)C(F)(F)F,0.0001372533562906347,0.00013496580117055152
+COCN(c1c(CC)cccc1CC)C(=O)CCl,0.05560351873894184,0.23816840526513422
+CC(OC(=O)C(c1ccc(cc1)Br)(c1ccc(cc1)Br)O)C,0.06073132568962639,0.060497742776698574
+Clc1ccccc1c1nnc(nn1)c1ccccc1Cl,0.06152148673385627,0.05706818624978773
 N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)(C)C,0.06360487171247954,0.06009909138187043
-CN1CN(C)CSC1=S,0.18486987933542975,0.027422365728598172
+CC(C1(C)N=C(NC1=O)c1nc2ccccc2cc1C(=O)O)C,0.06423944765895072,1.605986191473768
+O=C(N(C)C)Nc1cccc(c1)C(F)(F)F,0.06459882942614491,0.10157735340683115
+O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(c(c1F)Cl)F,0.06690994773808298,0.19325167158375256
+CCOc1ccc(cc1)C(COCc1cccc(c1)Oc1ccccc1)(C)C,0.0690593023384914,0.06773123883198195
+COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1Cl,0.06987675250196507,0.05590140200157206
+CSc1nnc(c(=O)n1N)C(C)(C)C,0.06999926640768805,0.06393266242893511
+O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(c(c1)Cl)OC(C(OC(F)(F)F)F)(F)F,0.07306609422899836,0.062106180868884746
+Clc1ccc(cc1)CN(C(=O)Nc1ccccc1)C1CCCC1,0.0760257762657501,0.5473855891134007
+COP(=O)(C(C(Cl)(Cl)Cl)O)OC,0.07768900686568829,0.05166319030658296
+O=C(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.08510674803234901,0.08272375649019124
+O=C1N(OCC1(C)C)Cc1ccccc1Cl,0.08969617860069455,0.41719152837532353
+Nc1nc(NC2CC2)nc(n1)N,0.09026150563412319,0.9387196585948812
 N#CC(c1ccccc1)(Cn1cncn1)CCc1ccc(cc1)Cl,0.10391366164191661,0.09203781459712614
-Clc1ccc(cc1)CCC(C(C)(C)C)(Cn1cncn1)O,0.05165383561566402,0.17867678986550448
-[O-][N+](=O)c1cc(Cl)c(c(c1)Cl)N,0.9419645496713847,0.5651787298028309
-CON(C(=O)Nc1ccc(cc1)Br)C,0.048243951057630914,0.014357399945172603
-COP(=S)(Oc1ccc(c(c1)C)[N+](=O)[O-])OC,0.001659247904766673,0.08332310268057162
 CCOc1cc(ccc1[N+](=O)[O-])Oc1ccc(cc1Cl)C(F)(F)F,0.11058877880543937,0.14653013191720715
-ClC(SN1C(=O)c2c(C1=O)cccc2)(Cl)Cl,0.6631652440985374,0.08430066662269543
-C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,0.1697708869122168,0.061250674376451514
-CN(C(=O)Oc1nc(nc(c1C)C)N(C)C)C,0.05203825886364726,0.05161859628615915
-CCC(Nc1c(cc(cc1[N+](=O)[O-])C(C)(C)C)[N+](=O)[O-])C,0.16929970598735858,0.13747135609511818
-Clc1ccc(cc1)CN(C(=O)Nc1ccccc1)C1CCCC1,0.0760257762657501,0.5473855891134007
+COC(=O)c1c(nc(c(c1CC(C)C)C1=NCCS1)C(F)(F)F)C(F)F,0.11151045196043953,0.11151045388522976
 Clc1cc(ccc1Oc1ccc(c(c1)C(=O)NS(=O)(=O)C)[N+](=O)[O-])C(F)(F)F,0.11395676083924232,0.011395676083924233
-CCCCCCCCSC(=O)Oc1cc(Cl)nnc1c1ccccc1,0.17813968959673715,0.3034972489425892
-Clc1ccccc1c1nnc(nn1)c1ccccc1Cl,0.06152148673385627,0.05706818624978773
-CCOC(=O)NCCOc1ccc(cc1)Oc1ccccc1,0.03318543029523152,0.08196801536106943
-CCCCOC(=O)C(Oc1ccc(cc1)Oc1ccc(cn1)C(F)(F)F)C,0.007825509706097071,0.009886227162529472
-CCNc1nc(NC(C)C)nc(n1)Cl,0.11591071091933607,0.056718974985359355
-CC(C(c1cncnc1)(c1ccc(cc1)OC(F)(F)F)O)C,0.038746408312020406,0.08469772512288609
-COP(=O)(C(C(Cl)(Cl)Cl)O)OC,0.07768900686568829,0.05166319030658296
+CCNc1nc(NC(C)C)nc(n1)Cl,0.11591071091933607,0.04636428436773443
+O=C(C1=C(C)OCCS1)Nc1ccccc1,0.1274956638724717,0.034848813981213346
+C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,0.13459866849613178,0.05350296944357954
 CCc1ccc(cc1)C(=O)NN(C(C)(C)C)C(=O)c1cc(C)cc(c1)C,0.13618183361575933,0.13731668655832788
-OC(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.21976935578028234,0.06424027322808253
-OC(=O)COc1ccc(cc1C)Cl,0.019938294964743114,0.0947069010825298
-Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,0.00694452873492003,0.01932390597300771
-CCOP(=O)(Oc1ccc(c(c1)C)SC)NC(C)C,0.005274306112287868,0.0035601567181414275
-CCOP(=S)(OCC)SCSP(=S)(OCC)OCC,0.005201883810203027,0.004681695305160139
-COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1C(=O)OC,0.655542030995076,0.31203800675365617
-CCCCC(c1ccc(cc1)Cl)(Cn1cncn1)C#N,0.03400568081866287,0.20067507097305953
-CC(OC(=O)C(c1ccc(cc1)Br)(c1ccc(cc1)Br)O)C,0.06073132568962639,0.060497742776698574
+CC(NC(=O)N1CC(=O)N(C1=O)c1cc(Cl)cc(c1)Cl)C,0.13932359364492994,0.03634528529867737
+OC(=O)COc1nc(Cl)c(cc1Cl)Cl,0.1403669879303106,0.0038990829980641837
+CCC(c1noc(c1)NC(=O)c1c(OC)cccc1OC)(CC)C,0.15252975563710267,1.6952764753748983
+CC(N1C(=O)c2ccccc2NS1(=O)=O)C,0.1560686482307559,0.14982590230152565
 Cc1nc(Nc2ccccc2)nc(c1)C1CC1,0.15801925526767843,0.15801924849469393
-O=C1OC(C(=O)N1Nc1ccccc1)(C)c1ccc(cc1)Oc1ccccc1,0.044873074905021335,0.045407278177700156
-N#Cc1c[nH]cc1c1cccc2c1OC(O2)(F)F,0.443217671652664,0.4553054263341003
-COC(=O)N(C(=O)N1COC2(C(=N1)c1ccc(cc1C2)Cl)C(=O)OC)c1ccc(cc1)OC(F)(F)F,0.006820319755914397,0.006820319575237628
-O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(c(c1)Cl)OC(C(OC(F)(F)F)F)(F)F,0.07306609422899836,0.062106180868884746
-Cc1ccc(cc1)N(S(=O)(=O)N(C)C)SC(Cl)(Cl)F,0.051834835094095484,0.27357274077439286
-CC(Cc1ccc(cc1)C(C)(C)C)CN1CC(C)OC(C1)C,0.005601647965290344,0.005601648122412352
-Fc1ccc(cc1)[Si](c1ccc(cc1)F)(Cn1cncn1)C,0.006341300659739408,0.023779877474022784
-CCOc1ccc(cc1)C(COCc1cccc(c1)Oc1ccccc1)(C)C,0.0690593023384914,0.06773123883198195
-CC(Oc1ccccn1)COc1ccc(cc1)Oc1ccccc1,0.4356352632556343,0.4315900691721648
-O=S1OCC2C(CO1)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.007126617932723449,0.015481963173347177
-OC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,0.00027647194701359843,0.0002764719511333511
-CCCSP(=O)(SCCC)OCC,0.011141416681473747,0.006065882533527741
-OC(=O)C(CCP(=O)(O)C)N,0.019323475195614302,1.2637552440957067
-CCSCSP(=S)(OCC)OCC,0.0006144925612602997,0.0006144925475253195
-CCOP(=S)(SCn1c(=O)oc2c1ccc(c2)Cl)OCC,0.0054376113486863924,0.0636200517424888
-CCOP(=S)(Oc1ncn(n1)c1ccccc1)OCC,0.004149212048673449,0.004149211896481245
-CNC(=O)Oc1cc(C)c(c(c1)C)SC,0.041276958181115306,0.1242747128033579
+Cc1cccc2c1n1cnnc1s2,0.16381576159162972,0.1373938645607217
+ClC(SN1C(=O)c2c(C1=O)cccc2)(Cl)Cl,0.16860133324539087,0.08430066662269543
+CCC(Nc1c(cc(cc1[N+](=O)[O-])C(C)(C)C)[N+](=O)[O-])C,0.16929970598735858,0.13747135609511818
+C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,0.1697708869122168,0.06904967382858089
+CCCCCCCCSC(=O)Oc1cc(Cl)nnc1c1ccccc1,0.17813968959673715,0.3034972489425892
+CN1CN(C)CSC1=S,0.18486987933542975,0.027422365728598172
+CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.18534506246313948,0.24799169923196304
+CNC(=O)Oc1cccc2c1cccc2,0.1878529324240324,0.2991731924668564
+COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1CCC(F)(F)F,0.19051986050321804,0.2098341392275743
+CC(Nc1nc(NC(C)C)nc(n1)Cl)C,0.21766590408142725,0.22201922216305578
+OC(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.21976935578028234,0.06424027322808253
+CCC(=O)Nc1ccc(c(c1)Cl)Cl,0.22010285589875195,0.041269285481015994
+CNC(=O)Oc1ccccc1OC(C)C,0.23895810443138246,0.22939978025412716
+CCC(n1c(=O)[nH]c(c(c1=O)Br)C)C,0.23935747721355113,0.39446112244793224
+OC(=O)c1nc(Cl)c(c(c1Cl)N)Cl,0.24848916516834604,0.9318343693812976
+C=CC1(C)OC(=O)N(C1=O)c1cc(Cl)cc(c1)Cl,0.25479642918707424,0.0186990521567307
+C#CCC1=C(C)C(CC1=O)OC(=O)C1C(C1(C)C)C=C(C)C,0.316253365684832,0.05492821614526029
+CC(=CC1C(C1(C)C)C(=O)OCc1coc(c1)Cc1ccccc1)C,0.3693416417277341,0.04432099700732809
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diff --git a/paper/data/swiss-cv.csv b/paper/data/swiss-cv.csv
new file mode 100644
index 0000000..309ba82
--- /dev/null
+++ b/paper/data/swiss-cv.csv
@@ -0,0 +1,447 @@
+SMILES,LOAEL_measured_median,LOAEL_predicted,Confidence
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diff --git a/paper/data/swiss-cv.id b/paper/data/swiss-cv.id
new file mode 100644
index 0000000..7c4d169
--- /dev/null
+++ b/paper/data/swiss-cv.id
@@ -0,0 +1 @@
+56c42c7e2b72ed1141000001
diff --git a/paper/data/swiss-test-predictions.csv b/paper/data/swiss-test-predictions.csv
index e29dfc8..b570805 100644
--- a/paper/data/swiss-test-predictions.csv
+++ b/paper/data/swiss-test-predictions.csv
@@ -1,150 +1,150 @@
-SMILES,LOAEL,Confidence,Dataset
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+Clc1c(Cl)c([N+](=O)[O-])c(c(c1Cl)Cl)Cl,0.47403843842257615,0.07983484928350948,1,swiss-prediction
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+Nc1nc(NC2CC2)nc(n1)N,0.5144905821145022,0.09185975434766332,1,swiss-prediction
+CCCCOCCOCCOCc1cc2OCOc2cc1CCC,0.5170806512852409,0.0683920470541598,0.1,swiss-prediction
+COCC(N(c1c(C)cccc1CC)C(=O)CCl)C,0.5285529966699751,0.09081986063352195,1,swiss-prediction
+O=Cc1ccco1,0.624453213155231,0.12486833177320306,0.1111111111111111,swiss-prediction
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+COC(=O)c1c(Cl)c(Cl)c(c(c1Cl)Cl)C(=O)OC,0.7681550277825105,0.2828087742644706,1,swiss-prediction
+COC(=O)NS(=O)(=O)c1ccc(cc1)N,0.7817895162025876,0.30649366939843004,1,swiss-prediction
+OC(=O)c1nc(Cl)c(c(c1Cl)N)Cl,0.8282972172278201,0.3802071151378962,1,swiss-prediction
+CC(C1(C)N=C(NC1=O)c1nc2ccccc2cc1C(=O)O)C,0.8351128195663594,0.2428137170977884,1,swiss-prediction
+COC(=O)Nc1nc2c([nH]1)cccc2,0.8499546987221808,0.06899951625487367,1,swiss-prediction
+CC(Oc1cccc(c1)NC(=O)c1ccccc1C(F)(F)F)C,0.9077984526598573,0.09329533119250691,1,swiss-prediction
+O=C(C1(C)CCCCC1)Nc1ccc(c(c1Cl)Cl)O,0.9662594125910484,0.31288159348953604,1,swiss-prediction
+ClCCP(=O)(O)O,0.9723587138566308,4.1102099979069795,0.16666666666666666,swiss-prediction
+COc1cccc(c1C)C(=O)NN(C(C)(C)C)C(=O)c1cc(C)cc(c1)C,1.1154252951100516,0.19454287979711238,1,swiss-prediction
+CCC(c1noc(c1)NC(=O)c1c(OC)cccc1OC)(CC)C,1.5854670852219546,0.1002087398883717,1,swiss-prediction
+OC(=O)COc1nc(F)c(c(c1Cl)N)Cl,1.9605490478397496,0.14404836568635776,1,swiss-prediction
+CC(OC(=O)Nc1cccc(c1)Cl)C,2.340158076742021,0.08873389182433254,1,swiss-prediction
+Oc1ccccc1c1ccccc1,3.119727015073393,0.1337230195551853,1,swiss-prediction
+OC(=O)CNCP(=O)(O)O,5.559726007239,0.700841565636653,0.16666666666666666,swiss-prediction
diff --git a/paper/data/swiss-test-predictions.id b/paper/data/swiss-test-predictions.id
new file mode 100644
index 0000000..fc28534
--- /dev/null
+++ b/paper/data/swiss-test-predictions.id
@@ -0,0 +1 @@
+56c429cc2b72ed0d79000004
diff --git a/paper/data/swiss.csv~ b/paper/data/swiss.csv~
deleted file mode 100644
index 997d8a4..0000000
--- a/paper/data/swiss.csv~
+++ /dev/null
@@ -1,494 +0,0 @@
-SMILES,LOAEL parental as dose (mmol/kg bw per day)
-[O-][N+]2=NC(N3C=CN=C3)=NC1=CC=C(Cl)C=C12,0.00020190555530632425
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-CC(C)OC(NC(C(C)C)C(NC(C1=CC=C(Cl)C=C1)CC(OC)=O)=O)=O,2.5070128670931195
-OC(CNCP(O)(O)=O)=O,7.180326992327815
-O=S(C1=C(S(CC)(=O)=O)N=C2N1C=CC=C2)(NC(NC3=NC(OC)=CC(OC)=N3)=O)=O,2.7556956072872962
-[O-]P(OCC)([H])=O.[O-]P(OCC)([H])=O.[O-]P(OCC)([H])=O.[Al+3],3.6853523538557287
-O=S(C1=C(C(N(C)C)=O)C=CC=N1)(NC(NC2=NC(OC)=CC(OC)=N2)=O)=O,5.08765706618306
diff --git a/paper/figure/corr-1.png b/paper/figure/corr-1.png
deleted file mode 100644
index 8c26052..0000000
--- a/paper/figure/corr-1.png
+++ /dev/null
diff --git a/paper/figure/crossvalidation.pdf b/paper/figure/crossvalidation.pdf
new file mode 100644
index 0000000..19a9913
--- /dev/null
+++ b/paper/figure/crossvalidation.pdf
diff --git a/paper/loael-dataset-correlation.pdf b/paper/figure/functional-groups.pdf
index 33dcfdf..65ca730 100644
--- a/paper/loael-dataset-correlation.pdf
+++ b/paper/figure/functional-groups.pdf
diff --git a/paper/figure/predictions-1.png b/paper/figure/predictions-1.png
deleted file mode 100644
index 759c327..0000000
--- a/paper/figure/predictions-1.png
+++ /dev/null
diff --git a/paper/figure/test-correlation.pdf b/paper/figure/test-correlation.pdf
new file mode 100644
index 0000000..835c927
--- /dev/null
+++ b/paper/figure/test-correlation.pdf
diff --git a/paper/figure/test-prediction.pdf b/paper/figure/test-prediction.pdf
new file mode 100644
index 0000000..f02330e
--- /dev/null
+++ b/paper/figure/test-prediction.pdf
diff --git a/paper/figure/unnamed-chunk-2-1.png b/paper/figure/unnamed-chunk-2-1.png
deleted file mode 100644
index 8c26052..0000000
--- a/paper/figure/unnamed-chunk-2-1.png
+++ /dev/null
diff --git a/paper/figure/unnamed-chunk-5-1.png b/paper/figure/unnamed-chunk-5-1.png
deleted file mode 100644
index 50699fd..0000000
--- a/paper/figure/unnamed-chunk-5-1.png
+++ /dev/null
diff --git a/paper/functional-groups.R b/paper/functional-groups.R
index 765817a..47cec0b 100755
--- a/paper/functional-groups.R
+++ b/paper/functional-groups.R
@@ -1,7 +1,8 @@
 library("ggplot2")
-#functional_groups <- read.csv("functional-groups-reduced.csv",header=F,row.names = 1)
-functional_groups <- read.csv("functional-groups-reduced4R.csv",header=F)
-print(functional_groups)
 
-ggplot(functional_groups,aes(x=V1,y=V2,fill=V3),legendTitle="Dataset") + geom_bar(stat="identity", position=position_dodge())  + xlab("") + ylab("") + coord_flip() 
-ggsave("functional-groups.pdf")
+functional_groups <- read.csv("data/functional-groups-reduced4R.csv",header=F)
+
+names(functional_groups) = c("V1","V2","Dataset")
+
+ggplot(functional_groups,aes(x=V1,y=V2,fill=Dataset)) + geom_bar(stat="identity", position=position_dodge())  + xlab("") + ylab("") + coord_flip() 
+ggsave("figure/functional-groups.pdf")
diff --git a/paper/loael-dataset-comparison-all-compounds.pdf b/paper/loael-dataset-comparison-all-compounds.pdf
deleted file mode 100644
index ee34390..0000000
--- a/paper/loael-dataset-comparison-all-compounds.pdf
+++ /dev/null
diff --git a/paper/loael-dataset-comparison-common-compounds.pdf b/paper/loael-dataset-comparison-common-compounds.pdf
deleted file mode 100644
index 4dba051..0000000
--- a/paper/loael-dataset-comparison-common-compounds.pdf
+++ /dev/null
diff --git a/paper/loael-dataset-comparison.rb b/paper/loael-dataset-comparison.rb
deleted file mode 100644
index 5850236..0000000
--- a/paper/loael-dataset-comparison.rb
+++ /dev/null
@@ -1,75 +0,0 @@
-require_relative '../../lazar/lib/lazar'
-include OpenTox
-
-old = Dataset.from_csv_file File.join(File.dirname(__FILE__),"..","regression","LOAEL_mg_corrected_smiles_mmol.csv")
-new = Dataset.from_csv_file File.join(File.dirname(__FILE__),"..","regression","swissRat_chron_LOAEL_mmol.csv")
-
-combined_compounds = old.compound_ids & new.compound_ids
-
-compound_vector = []
-value_vector = []
-dataset_vector = []
-
-old_median = []
-new_median = []
-
-errors = []
-combined_compounds.each do |cid|
-  c = Compound.find cid
-  old_values = old.values(c,old.features.first)
-  old_median << -Math.log(old_values.mean) 
-  old_values.each do |v|
-    compound_vector << c.smiles
-    value_vector << -Math.log(v.to_f)
-    dataset_vector << old.name
-  end
-  new_values = new.values(c,new.features.first)
-  new_median << -Math.log(new_values.mean)
-  new_values.each do |v|
-    compound_vector << c.smiles
-    value_vector << -Math.log(v)
-    dataset_vector << new.name
-  end
-end
-old_median.each_index do |i|
-  errors[i] = (old_median[i] - new_median[i]).abs unless old_median[i] == new_median[i]
-end
-rmse = 0
-mae = 0
-errors.compact.each do |e|
-  rmse += e**2
-  mae += e
-end
-rmse = Math.sqrt(rmse/errors.size)
-mae = mae/errors.size
-
-=begin
-R.assign "smi", compound_vector
-R.assign "values", value_vector
-R.assign "dataset", dataset_vector
-R.eval "df <- data.frame(factor(smi),values,factor(dataset))"
-R.eval "df$smi <- reorder(df$factor.smi,df$values)"
-R.eval "img <- ggplot(df, aes(smi,values,ymin = min(values), ymax=max(values),color=dataset))"
-R.eval "img <- img + ylab('-log(LOAEL mg/kg_bw/day)') + xlab('Compound') + theme(axis.text.x = element_blank())"
-R.eval "img <- img + geom_point()"
-
-R.eval "ggsave(file='/home/ch/opentox/lazar-nestec-data/loael-dataset-comparison-mmol_kg_day.svg', plot=img,width=12, height=8)"
-=end
-#img <- ggplot(data, aes(SMILES,LOAEL,ymin = min(LOAEL), ymax=max(LOAEL),color=Dataset))
-
-#img <- img + ylab('-log(LOAEL mg/kg_bw/day)') + xlab('Compound') + theme(axis.text.x = element_blank())
-
-#img <- img + geom_point()
-
-#print(img)
-
-R.assign "Mazzatorta", old_median
-R.assign "SwissFederalOffice", new_median
-R.eval "df <- data.frame(Mazzatorta,SwissFederalOffice)"
-R.eval "ggplot(df, aes(Mazzatorta,SwissFederalOffice)) + geom_point() + geom_abline(intercept=0.0) "
-R.eval "ggsave(file='/home/ch/src/lazar-nestec-data/paper/loael-dataset-correlation.pdf')"
-
-puts "Correlation Mazzatorta/SwissFederalOffice:"
-puts "\tr^2: #{R.eval("cor(Mazzatorta,SwissFederalOffice,use='complete')").to_f**2}"
-puts "\tRMSE: #{rmse}"
-puts "\tMAE: #{mae}"
diff --git a/paper/loael.Rmd b/paper/loael.Rmd
index a94e88a..29456a6 100644
--- a/paper/loael.Rmd
+++ b/paper/loael.Rmd
@@ -8,9 +8,8 @@ keywords: (Q)SAR, read-across, LOAEL
 date: \today
 abstract: " "
 documentclass: achemso
-bibliography: references.bib
+bibliography: references.bibtex
 bibliographystyle: achemso
-biblio-style: achemso
 output:
   pdf_document:
     fig_caption: yes
@@ -21,14 +20,36 @@ Introduction
 
 Christoph + Elena + Benoit
 
-The main objectives of this study are
+The quality and reproducibility of (Q)SAR and  read-across predictions is a controversial topic in the toxicological risk-assessment community. Although model predictions can be validated with various procedures it is rarely possible to put the results into the context of experimental variability, because replicate experiments are rarely available.
 
--   to investigate the experimental variability of LOAEL data
+With missing information about the variability of experimental toxicity data it is hard to judge the performance of predictive models and it is tempting for model developments to use aggressive model optimisation methods that lead to impressive validation results, but also to overfitted models with little practical relevance.
 
--   develop predictive model for lowest observed effect levels
+In this study we intent to compare model predictions with experimental variability with chronic oral rat lowest adverse effect levels (LOAEL) as toxicity endpoint.
+We are using two datasets, one from [@mazzatorta08] (*Mazzatorta* dataset) and one from the Swiss Federal Office of TODO (*Swiss Federal Office* dataset).
 
--   compare the performance of model predictions with experimental
-    variability
+Elena: do you have a reference and the name of the department?
+
+```{r echo=F}
+t = read.csv("data/test.csv")
+```
+
+`r length(unique(t$SMILES))` compounds are common in both datasets and we use them as a test set in our investigation. For this test set we will
+
+- compare the structural diversity of both datasets
+- compare the LOAEL values in both datasets
+- build prediction models based on the Mazzatorta, Swiss Federal Office datasets and a combination of both
+- predict LOAELs of the training set
+- compare predictions with experimental variability
+
+With this investigation we also want to support the idea of reproducible research, by providing all datasets and programs that have been used to generate this manuscript under a TODO license.
+
+A self-contained docker image with all program dependencies required for the reproduction of these results is available from TODO.
+
+Source code and datasets for the reproduction of this manuscript can be downloaded from the GitHub repository TODO. The lazar framework [@Maunz2013] is also available under a GPL License from https://github.com/opentox/lazar.
+
+TODO: github tags
+
+Elena: please check if this is publication strategy is ok for the Swiss Federal Office
 
 Materials and Methods
 =====================
@@ -36,69 +57,87 @@ Materials and Methods
 Datasets
 --------
 
+```{r echo=F}
+m = read.csv("data/mazzatorta.csv",header=T)
+s = read.csv("data/swiss.csv",header=T)
+t = read.csv("data/test.csv",header=T)
+c = read.csv("data/combined.csv",header=T)
+```
+
 ### Mazzatorta dataset
 
-Just referred to the paper 2008.
+The first dataset (*Mazzatorta* dataset for further reference) originates from
+the publication of [@mazzatorta08]. It contains chronic (> 180 days) lowest
+observed effect levels (LOAEL) for rats (*Rattus norvegicus*) after oral
+(gavage, diet, drinking water) administration.  The Mazzatorta dataset consists
+of `r length(m$SMILES)` LOAEL values for `r length(unique(m$SMILES))` unique
+chemical structures.
 
 ### Swiss Federal Office dataset
 
 Elena + Swiss Federal Office contribution (input)
 
-Only rat LOAEL values were used for the current investigation, because
-they correspond directly to the Mazzatorta dataset.
+The Swiss Federal Office dataset consists of `r length(s$SMILES)` LOAEL values
+for `r length(unique(s$SMILES))` unique chemical structures.
 
 ### Preprocessing
 
-Christoph
+Chemical structures in both datasets were initially represented as SMILES strings
+[@doi:10.1021/ci00057a005]. Syntactically incorrect and missing SMILES were
+generated from other identifiers (e.g names, CAS numbers). Unique smiles from the OpenBabel library [@OBoyle2011] were used for the identification of duplicated structures. 
+
+Studies with undefined or empty LOAEL entries were removed from the datasets. LOAEL values were converted to mmol/kg_bw/day units. For prediction, validation and visualisation purposes -log10 transformations are used.
 
-Chemical structures in both datasets are represented as SMILES strings
-(Weininger 1988). Syntactically incorrect and missing SMILES were
-generated from other identifiers (e.g names, CAS numbers) when possible.
-Studies with undefined (“0”) or empty LOAEL entries were removed for
-this study.
+David: please check if we have missed something
+
+### Derived datasets
+
+Two derived datasets were obtained from the original datasets: 
+
+The *test* dataset contains data of compounds that occur in both datasets. Exact duplications of LOAEL values were removed, because it is very likely that they originate from the same study. 
+The test dataset has `r length(t$SMILES)` LOAEL values for `r length(unique(t$SMILES))` unique chemical structures.
+
+The *combined* dataset is the union of the Mazzatorta and the Swiss Federal Office dataset and it is used to build predictive models. Exact LOAEL duplications were removed, as for the test dataset.
+The combined dataset has `r length(c$SMILES)` LOAEL values for `r length(unique(c$SMILES))` unique chemical structures.
 
 Algorithms
 ----------
 
-Christoph
-
-For this study we are using the modular lazar (*la*zy *s*tructure
-*a*ctivity *r*elationships) framework (Maunz et al. 2013) for model
+In this study we are using the modular lazar (*la*zy *s*tructure
+*a*ctivity *r*elationships) framework [@Maunz2013] for model
 development and validation.
 
 lazar follows the following basic workflow: For a given chemical
-structure it searches in a database for similar structures (neighbors)
-with experimental data, builds a local (Q)SAR model with these neighbors
-and uses this model to predict the unknown activity of the query
-compound. This procedure resembles an automated version of *read across*
+structure lazar 
+
+- searches in a database for similar structures (*neighbors*)
+with experimental data, 
+- builds a local QSAR model with these neighbors
+and 
+- uses this model to predict the unknown activity of the query
+compound.
+
+This procedure resembles an automated version of *read across*
 predictions in toxicology, in machine learning terms it would be
 classified as a *k-nearest-neighbor* algorithm.
 
 Apart from this basic workflow lazar is completely modular and allows
-the researcher to use any algorithm for neighbor identification and
-local (Q)SAR modelling. Within this study we are using the following
+the researcher to use any algorithm for similarity searches and
+local QSAR modelling. Within this study we are using the following
 algorithms:
 
 ### Neighbor identification
 
-Christoph
-
-Similarity calculations are based on MolPrint2D fingerprints (Bender et
-al. 2004) from the OpenBabel chemoinformatics library (OBoyle et al.
-2011).
+Similarity calculations are based on MolPrint2D fingerprints [@doi:10.1021/ci034207y] from the OpenBabel chemoinformatics library [@OBoyle2011].
 
 The MolPrint2D fingerprint uses atom environments as molecular
 representation, which resemble basically the chemical concept of
 functional groups. For each atom in a molecule it represents the
-chemical environment with the atom types of connected atoms.
+chemical environment using the atom types of connected atoms.
 
-The main advantage of MolPrint2D fingerprints over fingerprints with
-predefined substructures (such as OpenBabel FP3, FP4 or MACCs
-fingerprints) is that it may capture substructures of toxicological
-relevance that are not included in predefined substructure lists.
+MolPrint2D fingerprints are generated dynamically from chemical structures and do not rely on predefined lists of fragments (such as OpenBabel FP3, FP4 or MACCs fingerprints or lists of toxocophores/toxicophobes). This has the advantage the they may capture substructures of toxicological relevance that are not included in other fingerprints.
 Preliminary experiments have shown that predictions with MolPrint2D
-fingerprints are indeed more accurate than fingerprints with predefined
-substructures.
+fingerprints are indeed more accurate than other OpenBabel fingerprints.
 
 From MolPrint2D fingerprints we can construct a feature vector with all
 atom environments of a compound, which can be used to calculate chemical
@@ -106,27 +145,38 @@ similarities.
 
 [//]: # https://openbabel.org/docs/dev/FileFormats/MolPrint2D_format.html#molprint2d-format
 
-The chemical similarity between two compounds is expressed as the
-proportion between atom environments common in both structures and the
-total number of atom environments (Jaccard/Tanimoto index, [@eq:jaccard]).
+The chemical similarity between two compounds A and B is expressed as the
+proportion between atom environments common in both structures $A \cap B$ and the
+total number of atom environments $A \cup B$ (Jaccard/Tanimoto index, [@eq:jaccard]).
 
 $$ sim = \frac{|A \cap B|}{|A \cup B|} $$ {#eq:jaccard}
 
-$A$ atom environments of compound A, $B$ atom environments of compound B.
+A threshold of $sim < 0.1$ is used for the identification of neighbors for local QSAR models.
+Compounds with the same structure as the query structure are eliminated from the neighbors to obtain an unbiased prediction.
 
-### Local (Q)SAR models
+### Local QSAR models and predictions
 
-Christoph
+Only similar compounds (*neighbors*) are used for local QSAR models.
+In this investigation we are using a weighted partial least squares regression (PLS) algorithm for the prediction of quantitative properties.
+First all fingerprint features with identical values across all neighbors are removed.
+The reamining set of features is used as descriptors for creating a local weighted PLS model with atom environments as descriptors and model similarities as weights. The `plsr` function of the `pls` R package [@pls] is used for this purpose.
+Finally the local PLS model is applied to predict the activity of the query compound.
 
-As soon as neighbors for a query compound have been identified, we can
-use their experimental LOAEL values to predict the activity of the
-untested compound. In this case we are using the weighted mean of the
+If PLS modelling or prediction fails, the program resorts to using the weighted mean of the
 neighbors LOAEL values, where the contribution of each neighbor is
 weighted by its similarity to the query compound.
 
 ### Validation
 
-Christoph
+Two types of validations are used within this study:
+
+For the comparison of experimental variability with predictive accuracies we are using a test set of compounds that occur in both datasets. The *Mazzatorta*, *Swiss Federal Office* and *combined* datasets are used as training data for read across predictions. In order to obtain unbiased predictions *all* information from the test compound is removed from the training set prior to predictions. This procedure is hardcoded into the prediction algorithm in order to prevent validation errors.
+
+TODO: treatment of duplicates
+
+In addition traditional 10-fold crossvalidation results are provided. 
+
+Christoph: check if these specifications have changed at submission
 
 Results
 =======
@@ -147,7 +197,7 @@ baseline for evaluating prediction performance.
 Martin
 
 CheS-Mapper (Chemical Space Mapping and Visualization in 3D,
-http://ches-mapper.org/, (Gutlein, Karwath, and Kramer 2012)) can be
+http://ches-mapper.org/, @Gütlein2012) can be
 used to analyze the relationship between the structure of chemical
 compounds, their physico-chemical properties, and biological or toxic
 effects. CheS-Mapper embeds a dataset into 3D space, such that compounds
@@ -164,7 +214,7 @@ Christoph
 datasets. A complete table for 138 functional groups from OpenBabel FP4
 fingerprints can be found in the appendix.
 
-![Frequency of functional groups.](functional-groups.pdf){#fig:fg}
+![Frequency of functional groups.](figure/functional-groups.pdf){#fig:fg}
 
 ### Experimental variability versus prediction uncertainty 
 
@@ -179,6 +229,8 @@ substantial overlap of compounds, with LOAEL values in both datasets.
 
 ##### Intra dataset variability
 
+TODO: read data from files
+
 The Mazzatorta dataset has 562 LOAEL values with 439 unique structures,
 the Swiss Federal Office dataset has 493 rat LOAEL values with 381
 unique structures. [@fig:intra] shows the intra-dataset variability, where
@@ -188,7 +240,7 @@ similar in both datasets (p-value: 0.48).
 
 [//]: # p-value: 0.4750771581019402
 
-![Intra dataset variability: Each vertical line represents a compound, dots are individual LOAEL values.](loael-dataset-comparison-all-compounds.pdf){#fig:intra}
+[//]: # ![Intra dataset variability: Each vertical line represents a compound, dots are individual LOAEL values.](loael-dataset-comparison-all-compounds.pdf){#fig:intra}
 
 ##### Inter dataset variability
 
@@ -196,30 +248,29 @@ similar in both datasets (p-value: 0.48).
 and Swiss Federal Office datasets. Obviously the experimental
 variability is larger than for individual datasets.
 
-![Inter dataset variability](loael-dataset-comparison-common-compounds.pdf){#fig:inter}
+[//]: # ![Inter dataset variability](loael-dataset-comparison-common-compounds.pdf){#fig:inter}
 
 ##### LOAEL correlation between datasets
 
-[@fig:corr-1] depicts the correlation between LOAEL data from both datasets
+[@fig:corr] depicts the correlation between LOAEL data from both datasets
 (using means for multiple measurements).
 Identical values were removed from analysis.
 
 [//]: #   MAE: 0.801626064534318
 [//]: # with identical values
 
-```{r fig.cap="Correlation of dataset medians (-log10(LOAEL [mmol/kg_bw])", fig.lp="fig:", echo=F}
-library(ggplot2)
+
+```{r echo=F}
 data <- read.csv("data/common-median.csv",header=T)
-print(qplot(-log10(mazzatorta),-log10(swiss),data=data,xlab="Mazzatorta",ylab="Swiss Federal Office") + geom_point() + geom_abline(intercept=0.0) )
 cor <- cor.test(-log(data$mazzatorta),-log(data$swiss))
-median.r.square <- cor(-log(data$mazzatorta),-log(data$swiss),use='complete')**2
-median.rmse <- sqrt(mean((-log(data$mazzatorta)+log(data$swiss))^2))
+median.r.square <- round(cor(-log(data$mazzatorta),-log(data$swiss),use='complete')^2,2)
+median.rmse <- round(sqrt(mean((-log(data$mazzatorta)+log(data$swiss))^2)),2)
 ``` 
 
 Correlation analysis shows a
 significant correlation (p-value < 2.2e-16) with r\^2: `r round(median.r.square,2)`, RMSE: `r round(median.rmse,2)`
 
-### Local (Q)SAR models
+### Local QSAR models
 
 Christoph
 
@@ -227,39 +278,38 @@ In order to compare the perfomance of in silico models with experimental variabi
 
 The Mazzatorta, the Swiss Federal Office dataset and a combined dataset were used as training data. Predictions for the test set compounds were made after eliminating all information from the test compound from the corresponding training dataset. [@tbl:common-pred] summarizes the results:
 
+![Comparison of experimental with predicted LOAEL values, each vertical line represents a compound.](figure/test-prediction.pdf){#fig:comp}
+
 ```{r echo=F}
-validation <- read.csv("test-set-validation.csv",header=T)
+source("test-correlation.R")
 ```
-Training data | Model prediction | Experimental variability
---------------|------------------|-------------------------
-Mazzatorta | `r round(validation$rmse[1],2)`  | `r round(mazzatorta.rmse,2)`
-Swiss Federal Office |`r round(validation$rmse[2],2)`  | `r round(swiss.rmse,2)`
-Commmon | `r round(validation$rmse[3],2)`| `r common.rmse`
-Combined | | `r combined.rmse`
+
+Training data | $r^2$                     | RMSE
+--------------|---------------------------|-------------------------
+Experimental | `r median.r.square`      | `r median.rmse`
+Mazzatorta | `r mazzatorta.r_square`      | `r mazzatorta.rmse`
+Swiss Federal Office |`r swiss.r_square`  | `r swiss.rmse`
+Combined             | `r combined.r_square` | `r combined.rmse`
 
 : Comparison of model predictions with experimental variability. {#tbl:common-pred}
 
 ```{r echo=F}
-source("crossvalidations.R")
+source("crossvalidation.R")
 ```
 Traditional 10-fold cross-validation results are summarised in [@tbl:cv]:
 
-Training dataset | $r^2$ | RMSE | MAE
------------------|-------|------|----
-Mazzatorta | `r round(cv.mazzatorta.r.squared,2)`  | `r round(cv.mazzatorta.rmse,2)`| `r round(cv.mazzatorta.mae,2)`
-Swiss Federal Office | `r round(cv.swiss.r.squared,2)`  | `r round(cv.swiss.rmse,2)`| `r round(cv.swiss.mae,2)`
-Combined | `r round(cv.combined.r.squared,2)`  | `r round(cv.combined.rmse,2)`| `r round(cv.combined.mae,2)`
+Training dataset | $r^2$ | RMSE 
+-----------------|-------|------
+Mazzatorta | `r round(cv.mazzatorta.r_square,2)`  | `r round(cv.mazzatorta.rmse,2)`
+Swiss Federal Office | `r round(cv.swiss.r_square,2)`  | `r round(cv.swiss.rmse,2)`
+Combined | `r round(cv.combined.r_square,2)`  | `r round(cv.combined.rmse,2)`
 
 : 10-fold crossvalidation results {#tbl:cv}
 
-[//]: # ```{r fig.cap="Comparison of predictions with measured values (-log10(LOAEL [mmol/kg_bw])", fig.lp="fig:", echo=F}
+![Correlation of experimental with predicted LOAEL values (test set)](figure/test-correlation.pdf){}
+
+![Correlation of experimental with predicted LOAEL values (10-fold crossvalidation)](figure/crossvalidation.pdf){}
 
-```{r predictions, fig.cap='Comparison of predictions with measured values (-log10(LOAEL [mmol/kg_bw])', echo=F}
-library(ggplot2)
-data <- read.csv("data/common-test.csv",header=T)
-sorted = data[ order(-log10(data$LOAEL)), ]
-ggplot(sorted, aes(SMILES,-log10(LOAEL),ymin = min(-log10(LOAEL)), ymax=max(-log10(LOAEL)),color=Dataset)) + geom_point() + ylab('-log(LOAEL mg/kg_bw/day)') + xlab('Compound') + theme(axis.text.x = element_blank())
-``` 
 
 Discussion
 ==========
@@ -273,31 +323,3 @@ Summary
 
 References
 ==========
-
-Bender, Andreas, Hamse Y. Mussa, and Robert C. Glen, and Stephan
-Reiling. 2004. “Molecular Similarity Searching Using Atom Environments,
-Information-Based Feature Selection, and a Naïve Bayesian Classifier.”
-*Journal of Chemical Information and Computer Sciences* 44 (1): 170–78.
-doi:[10.1021/ci034207y](https://doi.org/10.1021/ci034207y).
-
-Gütlein, Martin, Andreas Karwath, and Stefan Kramer. 2012. “CheS-Mapper
-- Chemical Space Mapping and Visualization in 3D.” *Journal of
-Cheminformatics* 4 (1): 7.
-doi:[10.1186/1758-2946-4-7](https://doi.org/10.1186/1758-2946-4-7).
-
-Maunz, Andreas, Martin Gütlein, Micha Rautenberg, David Vorgrimmler,
-Denis Gebele, and Christoph Helma. 2013. “Lazar: A Modular Predictive
-Toxicology Framework.” *Frontiers in Pharmacology* 4. Frontiers Media
-SA.
-doi:[10.3389/fphar.2013.00038](https://doi.org/10.3389/fphar.2013.00038).
-
-OBoyle, Noel M, Michael Banck, Craig A James, Chris Morley, Tim
-Vandermeersch, and Geoffrey R Hutchison. 2011. “Open Babel: An Open
-Chemical Toolbox.” *Journal of Cheminformatics* 3 (1). Springer Science;
-Business Media: 33.
-doi:[10.1186/1758-2946-3-33](https://doi.org/10.1186/1758-2946-3-33).
-
-Weininger, David. 1988. “SMILES, a Chemical Language and Information
-System. 1. Introduction to Methodology and Encoding Rules.” *Journal of
-Chemical Information and Computer Sciences* 28 (1): 31–36.
-doi:[10.1021/ci00057a005](https://doi.org/10.1021/ci00057a005).
diff --git a/paper/loael.md b/paper/loael.md
index a2fc458..69ee1ff 100644
--- a/paper/loael.md
+++ b/paper/loael.md
@@ -8,9 +8,8 @@ keywords: (Q)SAR, read-across, LOAEL
 date: \today
 abstract: " "
 documentclass: achemso
-bibliography: references.bib
+bibliography: references.bibtex
 bibliographystyle: achemso
-biblio-style: achemso
 output:
   pdf_document:
     fig_caption: yes
@@ -21,14 +20,34 @@ Introduction
 
 Christoph + Elena + Benoit
 
-The main objectives of this study are
+The quality and reproducibility of (Q)SAR and  read-across predictions is a controversial topic in the toxicological risk-assessment community. Although model predictions can be validated with various procedures it is rarely possible to put the results into the context of experimental variability, because replicate experiments are rarely available.
 
--   to investigate the experimental variability of LOAEL data
+With missing information about the variability of experimental toxicity data it is hard to judge the performance of predictive models and it is tempting for model developments to use aggressive model optimisation methods that lead to impressive validation results, but also to overfitted models with little practical relevance.
 
--   develop predictive model for lowest observed effect levels
+In this study we intent to compare model predictions with experimental variability with chronic oral rat lowest adverse effect levels (LOAEL) as toxicity endpoint.
+We are using two datasets, one from [@mazzatorta08] (*Mazzatorta* dataset) and one from the Swiss Federal Office of TODO (*Swiss Federal Office* dataset).
 
--   compare the performance of model predictions with experimental
-    variability
+Elena: do you have a reference and the name of the department?
+
+
+
+155 compounds are common in both datasets and we use them as a test set in our investigation. For this test set we will
+
+- compare the structural diversity of both datasets
+- compare the LOAEL values in both datasets
+- build prediction models based on the Mazzatorta, Swiss Federal Office datasets and a combination of both
+- predict LOAELs of the training set
+- compare predictions with experimental variability
+
+With this investigation we also want to support the idea of reproducible research, by providing all datasets and programs that have been used to generate this manuscript under a TODO license.
+
+A self-contained docker image with all program dependencies required for the reproduction of these results is available from TODO.
+
+Source code and datasets for the reproduction of this manuscript can be downloaded from the GitHub repository TODO. The lazar framework [@Maunz2013] is also available under a GPL License from https://github.com/opentox/lazar.
+
+TODO: github tags
+
+Elena: please check if this is publication strategy is ok for the Swiss Federal Office
 
 Materials and Methods
 =====================
@@ -36,69 +55,82 @@ Materials and Methods
 Datasets
 --------
 
+
+
 ### Mazzatorta dataset
 
-Just referred to the paper 2008.
+The first dataset (*Mazzatorta* dataset for further reference) originates from
+the publication of [@mazzatorta08]. It contains chronic (> 180 days) lowest
+observed effect levels (LOAEL) for rats (*Rattus norvegicus*) after oral
+(gavage, diet, drinking water) administration.  The Mazzatorta dataset consists
+of 567 LOAEL values for 445 unique
+chemical structures.
 
 ### Swiss Federal Office dataset
 
 Elena + Swiss Federal Office contribution (input)
 
-Only rat LOAEL values were used for the current investigation, because
-they correspond directly to the Mazzatorta dataset.
+The Swiss Federal Office dataset consists of 493 LOAEL values
+for 381 unique chemical structures.
 
 ### Preprocessing
 
-Christoph
+Chemical structures in both datasets were initially represented as SMILES strings
+[@doi:10.1021/ci00057a005]. Syntactically incorrect and missing SMILES were
+generated from other identifiers (e.g names, CAS numbers). Unique smiles from the OpenBabel library [@OBoyle2011] were used for the identification of duplicated structures. 
+
+Studies with undefined or empty LOAEL entries were removed from the datasets. LOAEL values were converted to mmol/kg_bw/day units. For prediction, validation and visualisation purposes -log10 transformations are used.
+
+David: please check if we have missed something
 
-Chemical structures in both datasets are represented as SMILES strings
-(Weininger 1988). Syntactically incorrect and missing SMILES were
-generated from other identifiers (e.g names, CAS numbers) when possible.
-Studies with undefined (“0”) or empty LOAEL entries were removed for
-this study.
+### Derived datasets
+
+Two derived datasets were obtained from the original datasets: 
+
+The *test* dataset contains data of compounds that occur in both datasets. Exact duplications of LOAEL values were removed, because it is very likely that they originate from the same study. 
+The test dataset has 391 LOAEL values for 155 unique chemical structures.
+
+The *combined* dataset is the union of the Mazzatorta and the Swiss Federal Office dataset and it is used to build predictive models. Exact LOAEL duplications were removed, as for the test dataset.
+The combined dataset has 1014 LOAEL values for 671 unique chemical structures.
 
 Algorithms
 ----------
 
-Christoph
-
-For this study we are using the modular lazar (*la*zy *s*tructure
-*a*ctivity *r*elationships) framework (Maunz et al. 2013) for model
+In this study we are using the modular lazar (*la*zy *s*tructure
+*a*ctivity *r*elationships) framework [@Maunz2013] for model
 development and validation.
 
 lazar follows the following basic workflow: For a given chemical
-structure it searches in a database for similar structures (neighbors)
-with experimental data, builds a local (Q)SAR model with these neighbors
-and uses this model to predict the unknown activity of the query
-compound. This procedure resembles an automated version of *read across*
+structure lazar 
+
+- searches in a database for similar structures (*neighbors*)
+with experimental data, 
+- builds a local QSAR model with these neighbors
+and 
+- uses this model to predict the unknown activity of the query
+compound.
+
+This procedure resembles an automated version of *read across*
 predictions in toxicology, in machine learning terms it would be
 classified as a *k-nearest-neighbor* algorithm.
 
 Apart from this basic workflow lazar is completely modular and allows
-the researcher to use any algorithm for neighbor identification and
-local (Q)SAR modelling. Within this study we are using the following
+the researcher to use any algorithm for similarity searches and
+local QSAR modelling. Within this study we are using the following
 algorithms:
 
 ### Neighbor identification
 
-Christoph
-
-Similarity calculations are based on MolPrint2D fingerprints (Bender et
-al. 2004) from the OpenBabel chemoinformatics library (OBoyle et al.
-2011).
+Similarity calculations are based on MolPrint2D fingerprints [@doi:10.1021/ci034207y] from the OpenBabel chemoinformatics library [@OBoyle2011].
 
 The MolPrint2D fingerprint uses atom environments as molecular
 representation, which resemble basically the chemical concept of
 functional groups. For each atom in a molecule it represents the
-chemical environment with the atom types of connected atoms.
+chemical environment using the atom types of connected atoms.
 
-The main advantage of MolPrint2D fingerprints over fingerprints with
-predefined substructures (such as OpenBabel FP3, FP4 or MACCs
-fingerprints) is that it may capture substructures of toxicological
-relevance that are not included in predefined substructure lists.
+MolPrint2D fingerprints are generated dynamically from chemical structures and do not rely on predefined lists of fragments (such as OpenBabel FP3, FP4 or MACCs fingerprints or lists of toxocophores/toxicophobes). This has the advantage the they may capture substructures of toxicological relevance that are not included in other fingerprints.
 Preliminary experiments have shown that predictions with MolPrint2D
-fingerprints are indeed more accurate than fingerprints with predefined
-substructures.
+fingerprints are indeed more accurate than other OpenBabel fingerprints.
 
 From MolPrint2D fingerprints we can construct a feature vector with all
 atom environments of a compound, which can be used to calculate chemical
@@ -106,27 +138,38 @@ similarities.
 
 [//]: # https://openbabel.org/docs/dev/FileFormats/MolPrint2D_format.html#molprint2d-format
 
-The chemical similarity between two compounds is expressed as the
-proportion between atom environments common in both structures and the
-total number of atom environments (Jaccard/Tanimoto index, [@eq:jaccard]).
+The chemical similarity between two compounds A and B is expressed as the
+proportion between atom environments common in both structures $A \cap B$ and the
+total number of atom environments $A \cup B$ (Jaccard/Tanimoto index, [@eq:jaccard]).
 
 $$ sim = \frac{|A \cap B|}{|A \cup B|} $$ {#eq:jaccard}
 
-$A$ atom environments of compound A, $B$ atom environments of compound B.
+A threshold of $sim < 0.1$ is used for the identification of neighbors for local QSAR models.
+Compounds with the same structure as the query structure are eliminated from the neighbors to obtain an unbiased prediction.
 
-### Local (Q)SAR models
+### Local QSAR models and predictions
 
-Christoph
+Only similar compounds (*neighbors*) are used for local QSAR models.
+In this investigation we are using a weighted partial least squares regression (PLS) algorithm for the prediction of quantitative properties.
+First all fingerprint features with identical values across all neighbors are removed.
+The reamining set of features is used as descriptors for creating a local weighted PLS model with atom environments as descriptors and model similarities as weights. The `plsr` function of the `pls` R package [@pls] is used for this purpose.
+Finally the local PLS model is applied to predict the activity of the query compound.
 
-As soon as neighbors for a query compound have been identified, we can
-use their experimental LOAEL values to predict the activity of the
-untested compound. In this case we are using the weighted mean of the
+If PLS modelling or prediction fails, the program resorts to using the weighted mean of the
 neighbors LOAEL values, where the contribution of each neighbor is
 weighted by its similarity to the query compound.
 
 ### Validation
 
-Christoph
+Two types of validations are used within this study:
+
+For the comparison of experimental variability with predictive accuracies we are using a test set of compounds that occur in both datasets. The *Mazzatorta*, *Swiss Federal Office* and *combined* datasets are used as training data for read across predictions. In order to obtain unbiased predictions *all* information from the test compound is removed from the training set prior to predictions. This is hardcoded into the prediction algorithm in order to prevent validation errors.
+
+TODO: treatment of duplicates
+
+In addition traditional 10-fold crossvalidation results are provided. 
+
+Christoph: check if these specifications have changed at submission
 
 Results
 =======
@@ -147,7 +190,7 @@ baseline for evaluating prediction performance.
 Martin
 
 CheS-Mapper (Chemical Space Mapping and Visualization in 3D,
-http://ches-mapper.org/, (Gutlein, Karwath, and Kramer 2012)) can be
+http://ches-mapper.org/, @Gütlein2012) can be
 used to analyze the relationship between the structure of chemical
 compounds, their physico-chemical properties, and biological or toxic
 effects. CheS-Mapper embeds a dataset into 3D space, such that compounds
@@ -164,7 +207,7 @@ Christoph
 datasets. A complete table for 138 functional groups from OpenBabel FP4
 fingerprints can be found in the appendix.
 
-![Frequency of functional groups.](functional-groups.pdf){#fig:fg}
+![Frequency of functional groups.](figure/functional-groups.pdf){#fig:fg}
 
 ### Experimental variability versus prediction uncertainty 
 
@@ -177,6 +220,8 @@ substantial overlap of compounds, with LOAEL values in both datasets.
 
 ##### Intra dataset variability
 
+TODO: read data from files
+
 The Mazzatorta dataset has 562 LOAEL values with 439 unique structures,
 the Swiss Federal Office dataset has 493 rat LOAEL values with 381
 unique structures. [@fig:intra] shows the intra-dataset variability, where
@@ -186,7 +231,7 @@ similar in both datasets (p-value: 0.48).
 
 [//]: # p-value: 0.4750771581019402
 
-![Intra dataset variability: Each vertical line represents a compound, dots are individual LOAEL values.](loael-dataset-comparison-all-compounds.pdf){#fig:intra}
+[//]: # ![Intra dataset variability: Each vertical line represents a compound, dots are individual LOAEL values.](loael-dataset-comparison-all-compounds.pdf){#fig:intra}
 
 ##### Inter dataset variability
 
@@ -194,11 +239,11 @@ similar in both datasets (p-value: 0.48).
 and Swiss Federal Office datasets. Obviously the experimental
 variability is larger than for individual datasets.
 
-![Inter dataset variability](loael-dataset-comparison-common-compounds.pdf){#fig:inter}
+[//]: # ![Inter dataset variability](loael-dataset-comparison-common-compounds.pdf){#fig:inter}
 
 ##### LOAEL correlation between datasets
 
-[@fig:corr-1] depicts the correlation between LOAEL data from both datasets
+[@fig:corr] depicts the correlation between LOAEL data from both datasets
 (using means for multiple measurements).
 Identical values were removed from analysis.
 
@@ -206,16 +251,12 @@ Identical values were removed from analysis.
 [//]: # with identical values
 
 
-```
-## Loading required package: methods
-```
 
-![Correlation of dataset medians (-log10(LOAEL [mmol/kg_bw])](figure/unnamed-chunk-2-1.png)
 
 Correlation analysis shows a
-significant correlation (p-value < 2.2e-16) with r\^2: 0.55, RMSE: 1.34
+significant correlation (p-value < 2.2e-16) with r\^2: 0.58, RMSE: 1.3
 
-### Local (Q)SAR models
+### Local QSAR models
 
 Christoph
 
@@ -223,46 +264,34 @@ In order to compare the perfomance of in silico models with experimental variabi
 
 The Mazzatorta, the Swiss Federal Office dataset and a combined dataset were used as training data. Predictions for the test set compounds were made after eliminating all information from the test compound from the corresponding training dataset. [@tbl:common-pred] summarizes the results:
 
+![Comparison of experimental with predicted LOAEL values, each vertical line represents a compound.](figure/test-prediction.pdf){#fig:comp}
+
+
 
-Training data | Model prediction | Experimental variability
---------------|------------------|-------------------------
-Mazzatorta | 0.88  | 0.87
-Swiss Federal Office |0.65  | 0.76
-Commmon | 1.28| 0.8314774
-Combined | | 0.8242536
+Training data | $r^2$                     | RMSE
+--------------|---------------------------|-------------------------
+Experimental | 0.58      | 1.3
+Mazzatorta | 0.38      | 1.49
+Swiss Federal Office |0.38  | 1.47
+Combined             | 0.38 | 1.47
 
 : Comparison of model predictions with experimental variability. {#tbl:common-pred}
 
 
 Traditional 10-fold cross-validation results are summarised in [@tbl:cv]:
 
-Training dataset | $r^2$ | RMSE | MAE
------------------|-------|------|----
-Mazzatorta | 0.37  | 0.84| 0.65
-Swiss Federal Office | 0.25  | 0.75| 0.61
-Combined | 0.12  | 1.45| 1.21
+Training dataset | $r^2$ | RMSE 
+-----------------|-------|------
+Mazzatorta | 0.38  | 2.01
+Swiss Federal Office | 0.3  | 1.67
+Combined | 0.38  | 1.81
 
 : 10-fold crossvalidation results {#tbl:cv}
 
-[//]: # ```{r fig.cap="Comparison of predictions with measured values (-log10(LOAEL [mmol/kg_bw])", fig.lp="fig:", echo=F}
+![Correlation of experimental with predicted LOAEL values (test set)](figure/test-correlation.pdf){}
 
+![Correlation of experimental with predicted LOAEL values (10-fold crossvalidation)](figure/crossvalidation.pdf){}
 
-```
-## Warning in file(file, "rt"): cannot open file 'data/common-test.csv': No
-## such file or directory
-```
-
-```
-## Error in file(file, "rt"): cannot open the connection
-```
-
-```
-## Error in log10(data$LOAEL): non-numeric argument to mathematical function
-```
-
-```
-## Error in ggplot(sorted, aes(SMILES, -log10(LOAEL), ymin = min(-log10(LOAEL)), : object 'sorted' not found
-```
 
 Discussion
 ==========
@@ -276,31 +305,3 @@ Summary
 
 References
 ==========
-
-Bender, Andreas, Hamse Y. Mussa, and Robert C. Glen, and Stephan
-Reiling. 2004. “Molecular Similarity Searching Using Atom Environments,
-Information-Based Feature Selection, and a Naïve Bayesian Classifier.”
-*Journal of Chemical Information and Computer Sciences* 44 (1): 170–78.
-doi:[10.1021/ci034207y](https://doi.org/10.1021/ci034207y).
-
-Gütlein, Martin, Andreas Karwath, and Stefan Kramer. 2012. “CheS-Mapper
-- Chemical Space Mapping and Visualization in 3D.” *Journal of
-Cheminformatics* 4 (1): 7.
-doi:[10.1186/1758-2946-4-7](https://doi.org/10.1186/1758-2946-4-7).
-
-Maunz, Andreas, Martin Gütlein, Micha Rautenberg, David Vorgrimmler,
-Denis Gebele, and Christoph Helma. 2013. “Lazar: A Modular Predictive
-Toxicology Framework.” *Frontiers in Pharmacology* 4. Frontiers Media
-SA.
-doi:[10.3389/fphar.2013.00038](https://doi.org/10.3389/fphar.2013.00038).
-
-OBoyle, Noel M, Michael Banck, Craig A James, Chris Morley, Tim
-Vandermeersch, and Geoffrey R Hutchison. 2011. “Open Babel: An Open
-Chemical Toolbox.” *Journal of Cheminformatics* 3 (1). Springer Science;
-Business Media: 33.
-doi:[10.1186/1758-2946-3-33](https://doi.org/10.1186/1758-2946-3-33).
-
-Weininger, David. 1988. “SMILES, a Chemical Language and Information
-System. 1. Introduction to Methodology and Encoding Rules.” *Journal of
-Chemical Information and Computer Sciences* 28 (1): 31–36.
-doi:[10.1021/ci00057a005](https://doi.org/10.1021/ci00057a005).
diff --git a/paper/loael.pdf b/paper/loael.pdf
index c937d62..cef90e5 100644
--- a/paper/loael.pdf
+++ b/paper/loael.pdf
diff --git a/paper/references.bib b/paper/references.bibtex
index 6b40541..735a52f 100644
--- a/paper/references.bib
+++ b/paper/references.bibtex
@@ -1,5 +1,5 @@
 @Article{Gütlein2012,
-AUTHOR = {Gutlein, Martin and Karwath, Andreas and Kramer, Stefan},
+AUTHOR = {Gütlein, Martin and Karwath, Andreas and Kramer, Stefan},
 TITLE = {CheS-Mapper - Chemical Space Mapping and Visualization in 3D},
 JOURNAL = {Journal of Cheminformatics},
 VOLUME = {4},
@@ -83,3 +83,34 @@ eprint = {
   title = {Open Babel: An open chemical toolbox},
   journal = {Journal of Cheminformatics}
 }
+
+@article{mazzatorta08,
+author = {Paolo Mazzatorta and Manuel Dominguez Estevez and Myriam Coulet and Benoit Schilter},
+title = {Modeling Oral Rat Chronic Toxicity},
+journal = {Journal of Chemical Information and Modeling},
+volume = {48},
+number = {10},
+pages = {1949-1954},
+year = {2008},
+doi = {10.1021/ci8001974},
+    note ={PMID: 18803370},
+
+URL = { 
+        http://dx.doi.org/10.1021/ci8001974
+    
+},
+eprint = { 
+        http://dx.doi.org/10.1021/ci8001974
+    
+}
+
+}
+
+@Manual{pls,
+    title = {pls: Partial Least Squares and Principal Component Regression},
+    author = {Bjørn-Helge Mevik and Ron Wehrens and Kristian Hovde Liland},
+    year = {2015},
+    note = {R package version 2.5-0},
+    url = {https://CRAN.R-project.org/package=pls},
+  }
+
diff --git a/paper/test-correlation-plot.R b/paper/test-correlation-plot.R
new file mode 100644
index 0000000..0626bd4
--- /dev/null
+++ b/paper/test-correlation-plot.R
@@ -0,0 +1,21 @@
+library(ggplot2)
+library(grid)
+library(gridExtra)
+
+experimental <- read.csv("data/median-correlation.csv",header=T)
+p1 = qplot(-log10(mazzatorta),-log10(swiss),data=experimental,xlab="-log10(LOAEL Mazzatorta median)",ylab="-log10(LOAEL Swiss Federal Office median)",main="Experimental data") + geom_point() + geom_abline(intercept=0.0) + xlim(-1,4) + ylim(-1,4)
+
+mazzatorta = read.csv("data/mazzatorta-test-predictions.csv",header=T)
+swiss = read.csv("data/swiss-test-predictions.csv",header=T)
+combined = read.csv("data/combined-test-predictions.csv",header=T)
+
+p2 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=mazzatorta,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Mazzatorta") + geom_point() + geom_abline(intercept=0.0) + xlim(-1,4) + ylim(-1,4)
+
+p3 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=swiss,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Swiss Federal Office") + geom_point() + geom_abline(intercept=0.0)  + xlim(-1,4) + ylim(-1,4)
+
+p4 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=combined,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0)  + xlim(-1,4) + ylim(-1,4)
+
+pdf('figure/test-correlation.pdf')
+grid.arrange(p1,p2,p3,p4,ncol=2)
+dev.off()
+
diff --git a/paper/test-correlation.R b/paper/test-correlation.R
new file mode 100644
index 0000000..99d113a
--- /dev/null
+++ b/paper/test-correlation.R
@@ -0,0 +1,15 @@
+mazzatorta = read.csv("data/mazzatorta-test-predictions.csv",header=T)
+swiss = read.csv("data/swiss-test-predictions.csv",header=T)
+combined = read.csv("data/combined-test-predictions.csv",header=T)
+
+mazzatorta.p = round(cor.test(-log(mazzatorta$LOAEL_measured_median),-log(mazzatorta$LOAEL_predicted))$p.value,2)
+mazzatorta.r_square = round(cor(-log(mazzatorta$LOAEL_measured_median),-log(mazzatorta$LOAEL_predicted))^2,2)
+mazzatorta.rmse = round(sqrt(mean((-log(mazzatorta$LOAEL_measured_median)+log(mazzatorta$LOAEL_predicted))^2)),2)
+
+swiss.p = round(cor.test(-log(swiss$LOAEL_measured_median),-log(swiss$LOAEL_predicted))$p.value,2)
+swiss.r_square = round(cor(-log(swiss$LOAEL_measured_median),-log(swiss$LOAEL_predicted))^2,2)
+swiss.rmse = round(sqrt(mean((-log(swiss$LOAEL_measured_median)+log(swiss$LOAEL_predicted))^2)),2)
+
+combined.p = round(cor.test(-log(combined$LOAEL_measured_median),-log(combined$LOAEL_predicted))$p.value,2)
+combined.r_square = round(cor(-log(combined$LOAEL_measured_median),-log(combined$LOAEL_predicted))^2,2)
+combined.rmse = round(sqrt(mean((-log(combined$LOAEL_measured_median)+log(combined$LOAEL_predicted))^2)),2)
diff --git a/paper/test-prediction-plot.R b/paper/test-prediction-plot.R
new file mode 100644
index 0000000..c43737f
--- /dev/null
+++ b/paper/test-prediction-plot.R
@@ -0,0 +1,32 @@
+library(ggplot2)
+library(grid)
+library(gridExtra)
+
+mazzatorta = read.csv("data/mazzatorta-test-predictions.csv",header=T)
+swiss = read.csv("data/swiss-test-predictions.csv",header=T)
+combined = read.csv("data/combined-test-predictions.csv",header=T)
+test <- read.csv("data/test.csv",header=T)
+n = c("SMILES","LOAEL","Source")
+
+data = data.frame(factor(test$SMILES),test$LOAEL,factor(test$Dataset))
+names(data) = n
+data$Type = "experimental"
+maz = data.frame(factor(mazzatorta$SMILES),mazzatorta$LOAEL_predicted,factor(mazzatorta$Dataset))
+names(maz) = n
+maz$Type = "predicted"
+data = rbind(data,maz)
+swi = data.frame(factor(swiss$SMILES),swiss$LOAEL_predicted,factor(swiss$Dataset))
+names(swi) = n
+swi$Type = "predicted"
+data = rbind(data,swi)
+comb = data.frame(factor(combined$SMILES),combined$LOAEL_predicted,factor(combined$Dataset))
+names(comb) = n
+comb$Type = "predicted"
+data = rbind(data,comb)
+data$LOAEL = -log(data$LOAEL)
+data$SMILES <- reorder(data$SMILES,data$LOAEL)
+img <- ggplot(data, aes(SMILES,LOAEL,ymin = min(LOAEL), ymax=max(LOAEL),shape=Source,color=Type))
+img <- img + ylab('-log(LOAEL mg/kg_bw/day)') + xlab('Compound') + theme(axis.text.x = element_blank())
+img <- img + geom_point()
+
+ggsave(file='figure/test-prediction.pdf', plot=img,width=12, height=8)
diff --git a/paper/test-set-validation.rb b/paper/test-validation.rb
index d842d47..b748a8d 100644
--- a/paper/test-set-validation.rb
+++ b/paper/test-validation.rb
@@ -5,18 +5,20 @@ test = Dataset.from_csv_file(File.join(DATA,"common-test.csv"))
 file = File.join(DATA,ARGV[0])
 dataset = Dataset.from_csv_file file
 model = Model::LazarRegression.create dataset
-validation = Validation.create model, dataset, test
+validation = RegressionValidation.create model, dataset, test
 csv_file = file.sub(".csv","-test-predictions.csv")
+id_file = file.sub(".csv","-test-predictions.id")
+File.open(id_file,"w+"){|f| f.puts validation.id}
 name = File.basename(ARGV[0],".csv")
 
 data = []
 validation.predictions.each do |p|
-  data << [Compound.find(p[0]).smiles, p[2], p[3],"#{name}-prediction"]
+  data << [Compound.find(p[0]).smiles, p[1].median, p[2], p[3],"#{name}-prediction"]
 end
 
 data.sort!{|a,b| a[1] <=> b[1]}
 
 CSV.open(csv_file,"w+") do |csv|
-  csv << ["SMILES","LOAEL","Confidence","Dataset"]
+  csv << ["SMILES","LOAEL_measured_median","LOAEL_predicted","Confidence","Dataset"]
   data.each{|r| csv << r}
 end
diff --git a/paper/unique-smiles.rb b/paper/unique-smiles.rb
new file mode 100644
index 0000000..d316c3f
--- /dev/null
+++ b/paper/unique-smiles.rb
@@ -0,0 +1,18 @@
+require_relative "include.rb"
+
+input = Dataset.from_csv_file File.join(ARGV[0])
+outname = File.join(File.dirname(ARGV[0]),"#{ARGV[1]}.csv")
+
+data = []
+input.compounds.each_with_index do |cid,i|
+  c = Compound.find cid
+  v = input.data_entries[i].first
+  data << [c.smiles,v,ARGV[1]]
+end
+
+data.sort!{|a,b| a[1] <=> b[1]}
+
+CSV.open(outname,"w+") do |csv|
+  csv << ["SMILES","LOAEL","Dataset"]
+  data.each{|r| csv << r}
+end