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Diffstat (limited to 'mutagenicity.md')
| -rw-r--r-- | mutagenicity.md | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/mutagenicity.md b/mutagenicity.md index 9012ce5..5dbe124 100644 --- a/mutagenicity.md +++ b/mutagenicity.md @@ -474,10 +474,10 @@ Results 10-fold crossvalidations ------------------------ -Crossvalidation results are summarized in the following tables: @tbl:lazar shows `lazar` results with MolPrint2D and PaDEL descriptors, @tbl:R summarizes R results and @tbl:tensorflow Tensorflow results. +Crossvalidation results are summarized in the following tables: @tbl:lazar shows `lazar` results with MolPrint2D and PaDEL descriptors, @tbl:R R results and @tbl:tensorflow Tensorflow results. -```{#tbl:lazar .table file="tables/lazar-summary.csv" caption="Summary of lazar crossvalidation results"} +```{#tbl:lazar .table file="tables/lazar-summary.csv" caption="Summary of lazar crossvalidation results (all predictions/high confidence predictions"} ``` ```{#tbl:R .table file="tables/r-summary.csv" caption="Summary of R crossvalidation results"} @@ -488,6 +488,8 @@ Crossvalidation results are summarized in the following tables: @tbl:lazar shows @fig:roc depicts the position of all crossvalidation results in receiver operating characteristic (ROC) space. +{#fig:roc} + Confusion matrices for all models are available from the git repository http://git.in-silico.ch/mutagenicity-paper/10-fold-crossvalidations/confusion-matrices/, individual predictions can be found in http://git.in-silico.ch/mutagenicity-paper/10-fold-crossvalidations/predictions/. @@ -505,8 +507,6 @@ The most accurate crossvalidation predictions have been obtained with `lazar` mo : Summary of crossvalidation results. *R-RF*: R Random Forests, *R-SVM*: R Support Vector Machines, *R-DL*: R Deep Learning, *TF*: Tensorflow without feature selection, *TF-FS*: Tensorflow with feature selection, *L*: lazar, *L-HC*: lazar high confidence predictions, *L-P*: lazar with PaDEL descriptors, *L-P-HC*: lazar PaDEL high confidence predictions, *PPV*: Positive predictive value (Precision), *NPV*: Negative predictive value {#tbl:summary} -{#fig:roc} - R Models -------- |