typos fixed, pdf build

2 files changed, 14 insertions, 6 deletions

diff --git a/mutagenicity.md b/mutagenicity.md
index dd1aa77..896e088 100644
--- a/mutagenicity.md
+++ b/mutagenicity.md
@@ -654,10 +654,18 @@ with simple set operations.
 Pyrrolizidine alkaloid mutagenicity predictions
 -----------------------------------------------
 
-`lazar` models with MolPrint2D descriptors predicted {{lazar.mp2d.all.n_perc}}% of the pyrrolizidine alkaloids (PAs) ({{lazar.mp2d.high_confidence.n_perc}}% with high confidence), the remaining compounds are not within its applicability domain. All other models predicted 100% of the 602 compounds, indicating that all compounds are within their applicability domain.
-
-Mutagenicity predictions from different models show little agreement in general (table 4). 42 from 602 PAs have non-conflicting predictions (all of them non-mutagenic).
-Most models predict predominantly a non-mutagenic outcome for PAs, with exception of the R deep learning (DL) and the Tensorflow Scikit logistic regression models ({{pa.dl.mut_perc}} and {{pa.tf.lr_scikit.mut_perc}}% positive predictions). 
+`lazar` models with MolPrint2D descriptors predicted {{pa.lazar.mp2d.all.n_perc}}%
+of the pyrrolizidine alkaloids (PAs) ({{pa.lazar.mp2d.high_confidence.n_perc}}%
+with high confidence), the remaining compounds are not within its applicability
+domain. All other models predicted 100% of the 602 compounds, indicating that
+all compounds are within their applicability domain.
+
+Mutagenicity predictions from different models show little agreement in general
+(table 4). 42 from 602 PAs have non-conflicting predictions (all of them
+non-mutagenic).  Most models predict predominantly a non-mutagenic outcome for
+PAs, with exception of the R deep learning (DL) and the Tensorflow Scikit
+logistic regression models ({{pa.tf.dl.mut_perc}} and
+{{pa.tf.lr_scikit.mut_perc}}% positive predictions). 
 
 <!--
 non-conflicting CIDs
@@ -705,9 +713,9 @@ non-conflicting CIDs
 118701599
 -->
 
-R RF and SVM models favor very strongly non-mutagenic predictions (only {{pa.r.rf.mut_perc}} and {{pa.r.svm.mut_perc}} % mutagenic PAs), while Tensorflow models classify approximately half of the PAs as mutagenic (RF {{pa.tf.rf.mut_perc}}%, LR-sgd {{pa.tf.lr_sgd}}%, LR-scikit:{{pa.tf.lr_scikit.mut_perc}}, LR-NN:{{pa.tf.nn.mut_perc}}%). `lazar` models predict predominately non-mutagenicity, but to a lesser extend than R models (MP2D:{{pa.lazar.all.mut_perc}}, PaDEL:{{pa.lazar.padel.mut_perc}}).
+R RF and SVM models favor very strongly non-mutagenic predictions (only {{pa.r.rf.mut_perc}} and {{pa.r.svm.mut_perc}} % mutagenic PAs), while Tensorflow models classify approximately half of the PAs as mutagenic (RF {{pa.tf.rf.mut_perc}}%, LR-sgd {{pa.tf.lr_sgd}}%, LR-scikit:{{pa.tf.lr_scikit.mut_perc}}, LR-NN:{{pa.tf.nn.mut_perc}}%). `lazar` models predict predominately non-mutagenicity, but to a lesser extend than R models (MP2D:{{pa.lazar.mp2d.all.mut_perc}}, PaDEL:{{pa.lazar.padel.all.mut_perc}}).
 
-It is interesting to note, that different implementations of the same algorithm show little accordance in their prediction (see e.g R-RF vs. Tensorflow-RF and LR-sgd vs. LR-scikit in Table4 and @tab:pa-summary).
+It is interesting to note, that different implementations of the same algorithm show little accordance in their prediction (see e.g R-RF vs. Tensorflow-RF and LR-sgd vs. LR-scikit in Table 4 and @tbl:pa-summary).
 
 **TODO** **Verena, Philipp** habt ihr eine Erklaerung dafuer?
 
diff --git a/mutagenicity.pdf b/mutagenicity.pdf
index af2434a..a873f27 100644
--- a/mutagenicity.pdf
+++ b/mutagenicity.pdf