typos fixed

2 files changed, 2 insertions, 2 deletions

diff --git a/mutagenicity.md b/mutagenicity.md
index 9efecfd..3cba82b 100644
--- a/mutagenicity.md
+++ b/mutagenicity.md
@@ -42,7 +42,7 @@ Abstract
 Random forest, support vector machine, logistic regression, neural networks and k-nearest neighbor
 (`lazar`) algorithms, were applied to new *Salmonella* mutagenicity dataset
 with 8309 unique chemical structures. The best prediction accuracies in
-10-fold-crossvalidation were obtained with `lazar` models and Mol, that gave accuracies
+10-fold-crossvalidation were obtained with `lazar` models and MolPrint2D descriptors, that gave accuracies ({{lazar-high-confidence.acc_perc}}%)
 similar to the interlaboratory variability of the Ames test.
 
 **TODO**: PA results
@@ -753,7 +753,7 @@ A new public *Salmonella* mutagenicity training dataset with 8309 compounds was
 created and used it to train `lazar`, R and Tensorflow models with MolPrint2D
 and PaDEL descriptors. The best performance was obtained with `lazar` models
 using MolPrint2D descriptors, with prediction accuracies
-({{lazar-high-confidence.acc}}) comparable to the interlaboratory variability
+({{lazar-high-confidence.acc_perc}}%) comparable to the interlaboratory variability
 of the Ames test (80-85%). Models based on PaDEL descriptors had lower
 accuracies than MolPrint2D models, but only the `lazar` algorithm could use
 MolPrint2D descriptors.
diff --git a/mutagenicity.pdf b/mutagenicity.pdf
index 7c04ef7..e33d90c 100644
--- a/mutagenicity.pdf
+++ b/mutagenicity.pdf