From 87294b79e16e8f21446b8a232f06c956e6b7e81e Mon Sep 17 00:00:00 2001 From: Christoph Helma Date: Fri, 12 Feb 2016 13:05:19 +0100 Subject: initial paper layout --- create_nestec_models.rb | 23 +- dump/production/compounds.bson | Bin 1825035 -> 435458 bytes dump/production/crossvalidations.bson | Bin 226927 -> 17383 bytes dump/production/datasets.bson | Bin 2706802 -> 284301 bytes dump/production/features.bson | Bin 571 -> 429 bytes dump/production/models.bson | Bin 44423 -> 5555 bytes dump/production/open_tox_model_predictions.bson | Bin 754 -> 515 bytes dump/production/repeatedcrossvalidations.bson | Bin 444 -> 296 bytes dump/production/system.indexes.bson | Bin 636 -> 454 bytes dump/production/validations.bson | Bin 243773 -> 19344 bytes paper/Rakefile | 12 + paper/functional-groups-reduced.csv | 34 + paper/functional-groups-reduced4R.csv | 68 + paper/functional-groups.R | 7 + paper/functional-groups4R.rb | 9 + paper/loael-dataset-comparison.rb | 17 +- paper/loael-dataset-correlation.pdf | Bin 23237 -> 6050 bytes paper/loael-variability.rb | 4 +- paper/loael.html | 433 +---- paper/loael.md | 135 ++ paper/loael.pdf | Bin 164444 -> 322278 bytes paper/references.bib | 85 + paper/template.latex | 256 +++ regression/LOAEL-rat-combined.csv | 2122 +++++++++++------------ 24 files changed, 1745 insertions(+), 1460 deletions(-) create mode 100644 paper/Rakefile create mode 100644 paper/functional-groups-reduced.csv create mode 100644 paper/functional-groups-reduced4R.csv create mode 100755 paper/functional-groups.R create mode 100644 paper/functional-groups4R.rb create mode 100644 paper/loael.md create mode 100644 paper/references.bib create mode 100644 paper/template.latex diff --git a/create_nestec_models.rb b/create_nestec_models.rb index 4523250..d9fa299 100644 --- a/create_nestec_models.rb +++ b/create_nestec_models.rb @@ -1,20 +1,31 @@ require_relative '../lazar/lib/lazar' include OpenTox -$mongo.database.drop -$gridfs = $mongo.database.fs # recreate GridFS indexes +#$mongo.database.drop +#$gridfs = $mongo.database.fs # recreate GridFS indexes #=begin [ - "Rat_TD50.csv", - "Mouse_TD50.csv", + #"Rat_TD50.csv", + #"Mouse_TD50.csv", + #"LOAEL_mg_corrected_smiles_mmol.csv", + #"swissRat_chron_LOAEL_mmol.csv", "LOAEL-rat-combined.csv" #"LOAEL_mmol_corrected_smiles.csv", #"swissMouse_chron_LOAEL_mmol.csv", #"swissMultigen_LOAEL_mmol.csv", #"swissRat_chron_LOAEL_mmol.csv", + ].each do |file| file = File.join(File.dirname(__FILE__),"regression",file) - Model::Prediction.from_csv_file file + params = { + :prediction_algorithm => "OpenTox::Algorithm::Regression.local_pls_regression", + } + dataset = Dataset.from_csv_file file + model = Model::LazarRegression.create dataset, params + cv = RegressionCrossValidation.create model + puts cv.to_yaml + #Model::Prediction.from_csv_file file, params + #model = Model::LazarRegression.create dataset, params end #=end -`mongodump -h 127.0.0.1 -d production` +#`mongodump -h 127.0.0.1 -d production` #`mongorestore --host 127.0.0.1` diff --git a/dump/production/compounds.bson b/dump/production/compounds.bson index c1768d4..56a53d5 100644 Binary files a/dump/production/compounds.bson and b/dump/production/compounds.bson differ diff --git a/dump/production/crossvalidations.bson b/dump/production/crossvalidations.bson index 4f836f6..259d194 100644 Binary files a/dump/production/crossvalidations.bson and b/dump/production/crossvalidations.bson differ diff --git a/dump/production/datasets.bson b/dump/production/datasets.bson index 1a6cd5d..7ffebd2 100644 Binary files a/dump/production/datasets.bson and b/dump/production/datasets.bson differ diff --git a/dump/production/features.bson b/dump/production/features.bson index d121c25..5315f87 100644 Binary files a/dump/production/features.bson and b/dump/production/features.bson differ diff --git a/dump/production/models.bson b/dump/production/models.bson index e8eacc2..072bff5 100644 Binary files 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b/dump/production/validations.bson differ diff --git a/paper/Rakefile b/paper/Rakefile new file mode 100644 index 0000000..1d45b9e --- /dev/null +++ b/paper/Rakefile @@ -0,0 +1,12 @@ +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/functional-groups-reduced.csv b/paper/functional-groups-reduced.csv new file mode 100644 index 0000000..525acbf --- /dev/null +++ b/paper/functional-groups-reduced.csv @@ -0,0 +1,34 @@ +Alkene,39,30 +Alkyne,5,8 +Alcohol,44,27 +Dialkylether,35,32 +Amine,66,41 +Aldehyde,3,1 +Ketone,21,25 +Enol,4,5 +Carboxylic_acid,33,38 +Lactone,11,10 +Carboxylic_acid_derivative,215,227 +Amide,38,60 +Lactam,12,18 +Amidine,3,4 +Nitrile,35,39 +Vinylogous_carbonyl_or_carboxyl_derivative,70,97 +Vinylogous_ester,113,120 +Carbonic_acid_derivatives,109,131 +Phenol,27,9 +Arylchloride,142,163 +Arylfluoride,22,41 +Heteroaromatic,147,205 +Nitro,42,31 +Sulfonic_derivative,24,29 +Sulfenic_derivative,48,34 +Phosphoric_acid_derivative,70,44 +Aromatic,402,396 +Heterocyclic,228,272 +Epoxide,11,2 +Annelated_rings,97,73 +Conjugated_double_bond,207,213 +Trifluoromethyl,44,63 +C_ONS_bond,520,484 +Salt,26,45 \ No newline at end of file diff --git a/paper/functional-groups-reduced4R.csv b/paper/functional-groups-reduced4R.csv new file mode 100644 index 0000000..e37c41d --- /dev/null +++ b/paper/functional-groups-reduced4R.csv @@ -0,0 +1,68 @@ +Alkene, 39, Mazzatorta +Alkene, 30, Swiss Federal Office +Alkyne, 5, Mazzatorta +Alkyne, 8, Swiss Federal Office +Alcohol, 44, Mazzatorta +Alcohol, 27, Swiss Federal Office +Dialkylether, 35, Mazzatorta +Dialkylether, 32, Swiss Federal Office +Amine, 66, Mazzatorta +Amine, 41, Swiss Federal Office +Aldehyde, 3, Mazzatorta +Aldehyde, 1, Swiss Federal Office +Ketone, 21, Mazzatorta +Ketone, 25, Swiss Federal Office +Enol, 4, Mazzatorta +Enol, 5, Swiss Federal Office +Carboxylic_acid, 33, Mazzatorta +Carboxylic_acid, 38, Swiss Federal Office +Lactone, 11, Mazzatorta +Lactone, 10, Swiss Federal Office +Carboxylic_acid_derivative, 215, Mazzatorta +Carboxylic_acid_derivative, 227, Swiss Federal Office +Amide, 38, Mazzatorta +Amide, 60, Swiss Federal Office +Lactam, 12, Mazzatorta +Lactam, 18, Swiss Federal Office +Amidine, 3, Mazzatorta +Amidine, 4, Swiss Federal Office +Nitrile, 35, Mazzatorta +Nitrile, 39, Swiss Federal Office +Vinylogous_carbonyl_or_carboxyl_derivative, 70, Mazzatorta +Vinylogous_carbonyl_or_carboxyl_derivative, 97, Swiss Federal Office +Vinylogous_ester, 113, Mazzatorta +Vinylogous_ester, 120, Swiss Federal Office +Carbonic_acid_derivatives, 109, Mazzatorta +Carbonic_acid_derivatives, 131, Swiss Federal Office +Phenol, 27, Mazzatorta +Phenol, 9, Swiss Federal Office +Arylchloride, 142, Mazzatorta +Arylchloride, 163, Swiss Federal Office +Arylfluoride, 22, Mazzatorta +Arylfluoride, 41, Swiss Federal Office +Heteroaromatic, 147, Mazzatorta +Heteroaromatic, 205, Swiss Federal Office +Nitro, 42, Mazzatorta +Nitro, 31, Swiss Federal Office +Sulfonic_derivative, 24, Mazzatorta +Sulfonic_derivative, 29, Swiss Federal Office +Sulfenic_derivative, 48, Mazzatorta +Sulfenic_derivative, 34, Swiss Federal Office +Phosphoric_acid_derivative, 70, Mazzatorta +Phosphoric_acid_derivative, 44, Swiss Federal Office +Aromatic, 402, Mazzatorta +Aromatic, 396, Swiss Federal Office +Heterocyclic, 228, Mazzatorta +Heterocyclic, 272, Swiss Federal Office +Epoxide, 11, Mazzatorta +Epoxide, 2, Swiss Federal Office +Annelated_rings, 97, Mazzatorta +Annelated_rings, 73, Swiss Federal Office +Conjugated_double_bond, 207, Mazzatorta +Conjugated_double_bond, 213, Swiss Federal Office +Trifluoromethyl, 44, Mazzatorta +Trifluoromethyl, 63, Swiss Federal Office +C_ONS_bond, 520, Mazzatorta +C_ONS_bond, 484, Swiss Federal Office +Salt, 26, Mazzatorta +Salt, 45, Swiss Federal Office diff --git a/paper/functional-groups.R b/paper/functional-groups.R new file mode 100755 index 0000000..765817a --- /dev/null +++ b/paper/functional-groups.R @@ -0,0 +1,7 @@ +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") diff --git a/paper/functional-groups4R.rb b/paper/functional-groups4R.rb new file mode 100644 index 0000000..1bb9a55 --- /dev/null +++ b/paper/functional-groups4R.rb @@ -0,0 +1,9 @@ +require 'csv' +csv = [] +CSV.foreach("functional-groups-reduced.csv") do |row| + csv << [row[0],row[1].to_i,"Mazzatorta"] + csv << [row[0],row[2].to_i,"Swiss Federal Office"] +end + +#File.open("functional-groups-reduced4R.csv","w+"){|f| f.puts csv.to_csv} +puts csv.collect{|r| r.join ", "}.join("\n") diff --git a/paper/loael-dataset-comparison.rb b/paper/loael-dataset-comparison.rb index c1602ad..815018d 100644 --- a/paper/loael-dataset-comparison.rb +++ b/paper/loael-dataset-comparison.rb @@ -57,17 +57,14 @@ 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 -R.assign "old", old_median -R.assign "new", new_median +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')" =begin -R.eval "df <- data.frame(old,new)" -R.eval "img <- ggplot(df, aes(old,new))" -R.eval "img <- img + geom_point()" -#R.eval "img <- img + geom_smooth(method=lm) " -R.eval "img <- img + geom_abline(intercept=0.0) " -R.eval "ggsave(file='/home/ch/opentox/lazar-nestec-data/loael-dataset-correlation.svg', plot=img,width=12, height=8)" =end -puts "Correlation old/new:" -puts "\tr^2: #{R.eval("cor(old,new,use='complete')").to_f**2}" +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-dataset-correlation.pdf b/paper/loael-dataset-correlation.pdf index 3e0a042..7cf219b 100644 Binary files a/paper/loael-dataset-correlation.pdf and b/paper/loael-dataset-correlation.pdf differ diff --git a/paper/loael-variability.rb b/paper/loael-variability.rb index ae74c3f..5a8f1ca 100644 --- a/paper/loael-variability.rb +++ b/paper/loael-variability.rb @@ -1,4 +1,4 @@ -require_relative '../lazar/lib/lazar' +require_relative '../../lazar/lib/lazar' include OpenTox #$mongo.database.drop #$gridfs = $mongo.database.fs # recreate GridFS indexes @@ -42,7 +42,7 @@ vars = [] #end #end end -#p vars +p vars # TODO statistical test for variances R.assign "vars1", vars[0] R.assign "vars2", vars[1] diff --git a/paper/loael.html b/paper/loael.html index c8c0a2d..95932e1 100644 --- a/paper/loael.html +++ b/paper/loael.html @@ -1,384 +1,55 @@ - - - - - - - - - - -
- - -
-

Introduction

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Data

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LOAEL Values

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Comparison of variances in datasets

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================================================================================================================================ lazar read across models for lowest adverse effect levels: A comparison of experimental variability with read across predictions ================================================================================================================================

+

Christoph Helma, David Vorgrimmler, Martin Guetlein, Denis Gebele, Elena Lo Piparo

+

in silico toxicology gmbh, Rastatterstrasse 41, 4051 Basel, Switzerland

+

Introduction

+

The main objectives of this study are

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    +
  • to investigate the experimental variability of LOAEL data
    • +
  • develop predictive model for lowest observed effect levels
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  • compare the performance of model predictions with experimental variability
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+

Methods

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Data

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Mazzatorta dataset

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Swiss dataset

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Preprocessing

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Missing and invalid SMILES Unfortunately no identifier is complete across all compound therefore we focused on SMILES. Missing SMILES were generated from other identifiers when available.

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study type/ table rat_chron mouse_chron multigen missing SMILES 35 27 31 invalid SMILES 9 6 9 corrected SMILES 44 33 40 Detailed tables: https://docs.google.com/spreadsheets/d/14P8F-3iX5gr5FbN7oSeuwabUOr_xdDhhr5KwiUX6LXY/edit?usp=sharing

+

Algorithms

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For this study we are using the modular lazar (lazy structure activity relationships) framework (helma..) 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 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 algorithms:

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Neighbor identification

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Similarity calculations are based on TODO fingerprints (Bender 2003) from the OpenBabel chemoinformatics library (TODO). The TODO fingerprint uses atom environments as molecular representation, which resemble basically the chemical concept of functional groups. For each atom in a molecule the atom types of connected atoms are recorded. The same procedure is repeated for connected atoms up to a given distance of chemical bonds. From this data a vector with atom type counts at a given distance from the central atom is constructed. These vectors are used to calculate chemical similarities.

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TODO: example???

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Similarities are expressed as Tanimoto index

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TODO: Jaquard index? TODO: formula TODO: similarity threshold

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Such a In machine learning

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The main advantage of TODO fingerprints in comparison to fingerprints with predefined substructures such as MACCs fingerprints (TODO) is that

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TODO; toxicological relevance

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Preliminary experiments have shown that predictions with TODO fingerprints are more accurate than fingerprints with predefined substructures (OpenBabel FP TODO) fingerprints, which is in agreement with findings in the literature (TOCDO cite).

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Local (Q)SAR models

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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 (TODO median?) of the neighbors LOAEL values, where the contribution of each neighbor is weighted by its similarity to the query compound.

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Validation

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Results

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Dataset comparison

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Structural composition

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Ches-Mapper analysis
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Distribution of functional groups
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LOAEL values

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Intra dataset variability

p-value: 0.4750771581019402

- -loael-variance.svg -
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Comparison of compounds in both datasets

- -loael-dataset-comparison-mmol_kg_day.svg -
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Correlation between datasets

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.. image:: loael-dataset-comparison-all-compounds.pdf

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Inter dataset variability
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.. image:: loael-dataset-comparison-common-compounds.pdf

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LOAEL correlation between datasets

using means

- -loael-dataset-correlation.svg +

.. image:: loael-dataset-correlation.pdf

with "identical" values

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-r^2: 0.6106457754533314 -RMSE: 1.2228212261024438 -MAE: 0.801626064534318
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Models

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Validation

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Discussion

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r^2: 0.6106457754533314 RMSE: 1.2228212261024438 MAE: 0.801626064534318

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Read across predictions

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Discussion

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Chemical similarity

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LOAEL variability

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Predictive performance

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diff --git a/paper/loael.md b/paper/loael.md new file mode 100644 index 0000000..2bb2add --- /dev/null +++ b/paper/loael.md @@ -0,0 +1,135 @@ +--- +title: "lazar read across models for lowest adverse effect levels: A comparison of experimental variability with read across predictions" +author: Christoph Helma^1^, David Vorgrimmler^1^, Denis Gebele^1^, Elena Lo Piparo^2^ +E-mail: helma@in-silico.ch +include-before: ^1^ in silico toxicology gmbh, Basel, Switzerland\newline^2^ Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland +keywords: (Q)SAR, read-across, LOAEL +date: \today +abstract: " " +documentclass: achemso +bibliography: references.bib +bibliographystyle: achemso +biblio-style: achemso +--- + +# Introduction + +The main objectives of this study are + + - to investigate the experimental variability of LOAEL data + - develop predictive model for lowest observed effect levels + - compare the performance of model predictions with experimental variability + +# Methods + +## Data + +### Mazzatorta dataset + +### Swiss Federal Office dataset + + Only rat LOAEL values were used for the current investigation, because they correspond directly to the Mazzatorta dataset. + +### Preprocessing + + Chemical structures in both datasets are represented as SMILES strings [@doi:10.1021/ci00057a005]. 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. + +## Algorithms + + For 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* 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 algorithms: + +### Neighbor identification + + 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. + + 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. + Preliminary experiments have shown that predictions with MolPrint2D fingerprints are indeed more accurate than fingerprints with predefined substructures. + + From MolPrint2D fingerprints we can construct a feature vector with all atom environments of a compound, which can be used to calculate chemical 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 (@sim)). + + (@sim) $sim = \frac{|A \cap B|}{|A \cup B|}$, $A$ atom environments of compound A, $B$ atom environments of compound B. + + +### Local (Q)SAR models + +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 neighbors LOAEL values, where the contribution of each neighbor is weighted by its similarity to the query compound. + +### Validation + +# Results + +### Dataset comparison + + The main objective of this section is to compare the content of both databases in terms of structural composition and LOAEL values, to estimate the experimental variability of LOAEL values and to establish a baseline for evaluating prediction performance. + +#### Structural composition + +##### Ches-Mapper analysis + + CheS-Mapper (Chemical Space Mapping and Visualization in 3D, 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 with similar feature values are close to each other. + The following two screenshots visualise the comparison. The datasets are embeded into 3D Space based on structural fragments from three Smart list (OpenBabel FP3, OpenBabel FP4 and OpenBabel MACCS). + +##### Distribution of functional groups + + Figure 1 shows the frequency of selected functional groups in both 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) + +#### LOAEL values + + Duplicated LOAEL values can be found in both datasets and there is a substantial overlap of compounds, with LOAEL values in both datasets. + +##### Intra dataset variability + + 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. Figure 2 shows the intra-dataset variability, where each vertical line represents a single compound and each dot represents an individual LOAEL value. The experimental variance of LOAEL values is 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) + +##### Inter dataset variability + + Figure 3 shows the same situation for the combination of the Mazzatorta and Swiss Federal Office datasets. Obviously the experimental variability is larger than for individual datasets. + +![Inter dataset variability](loael-dataset-comparison-common-compounds.pdf) + +##### LOAEL correlation between datasets + + Figure 4 depicts the correlation between LOAEL data from both datasets (using means for multiple measurements). Correlation analysis shows a significant correlation with r^2: 0.61, RMSE: 1.22, MAE: 0.80 + + + +[//]: # MAE: 0.801626064534318 +[//]: # with identical values + +![LOAEL correlation](loael-dataset-correlation.pdf) + + +### Read across predictions + +# Discussion + +### Chemical similarity + +### LOAEL variability + +### Predictive performance + +### + +# Summary + + $var1$ + +# References diff --git a/paper/loael.pdf b/paper/loael.pdf index accb381..d77517d 100644 Binary files a/paper/loael.pdf and b/paper/loael.pdf differ diff --git a/paper/references.bib b/paper/references.bib new file mode 100644 index 0000000..6b40541 --- /dev/null +++ b/paper/references.bib @@ -0,0 +1,85 @@ +@Article{Gütlein2012, +AUTHOR = {Gutlein, Martin and Karwath, Andreas and Kramer, Stefan}, +TITLE = {CheS-Mapper - Chemical Space Mapping and Visualization in 3D}, +JOURNAL = {Journal of Cheminformatics}, +VOLUME = {4}, +YEAR = {2012}, +NUMBER = {1}, +PAGES = {7}, +URL = {http://www.jcheminf.com/content/4/1/7}, +DOI = {10.1186/1758-2946-4-7}, +PubMedID = {22424447}, +ISSN = {1758-2946}, +ABSTRACT = {Analyzing chemical datasets is a challenging task for scientific researchers in the field of chemoinformatics. It is important, yet difficult to understand the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. To that respect, visualization tools can help to better comprehend the underlying correlations. Our recently developed 3D molecular viewer CheS-Mapper (Chemical Space Mapper) divides large datasets into clusters of similar compounds and consequently arranges them in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kind of features, like structural fragments as well as quantitative chemical descriptors. These features can be highlighted within CheS-Mapper, which aids the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. As a final function, the tool can also be used to select and export specific subsets of a given dataset for further analysis.}, +} + +@article{doi:10.1021/ci034207y, +author = {Andreas Bender and Hamse Y. Mussa, and and Robert C. Glen and Stephan Reiling}, +title = {Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier}, +journal = {Journal of Chemical Information and Computer Sciences}, +volume = {44}, +number = {1}, +pages = {170-178}, +year = {2004}, +doi = {10.1021/ci034207y}, + note ={PMID: 14741025}, + +URL = { + http://dx.doi.org/10.1021/ci034207y + +}, +eprint = { + http://dx.doi.org/10.1021/ci034207y + +} + +} + + +@article{Maunz2013, + doi = {10.3389/fphar.2013.00038}, + url = {http://dx.doi.org/10.3389/fphar.2013.00038}, + year = {2013}, + publisher = {Frontiers Media {SA}}, + volume = {4}, + author = {Andreas Maunz and Martin G\"{u}tlein and Micha Rautenberg and David Vorgrimmler and Denis Gebele and Christoph Helma}, + title = {lazar: a modular predictive toxicology framework}, + journal = {Frontiers in Pharmacology} +} + + + + +@article{doi:10.1021/ci00057a005, +author = {David Weininger}, +title = {SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules}, +journal = {Journal of Chemical Information and Computer Sciences}, +volume = {28}, +number = {1}, +pages = {31-36}, +year = {1988}, +doi = {10.1021/ci00057a005}, + +URL = { + http://dx.doi.org/10.1021/ci00057a005 + +}, +eprint = { + http://dx.doi.org/10.1021/ci00057a005 + +} + +} + +@article{OBoyle2011, + doi = {10.1186/1758-2946-3-33}, + url = {http://dx.doi.org/10.1186/1758-2946-3-33}, + year = {2011}, + publisher = {Springer Science and Business Media}, + volume = {3}, + number = {1}, + pages = {33}, + author = {Noel M OBoyle and Michael Banck and Craig A James and Chris Morley and Tim Vandermeersch and Geoffrey R Hutchison}, + title = {Open Babel: An open chemical toolbox}, + journal = {Journal of Cheminformatics} +} diff --git a/paper/template.latex b/paper/template.latex new file mode 100644 index 0000000..afa679a --- /dev/null +++ b/paper/template.latex @@ -0,0 +1,256 @@ 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