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| author | Christoph Helma <helma@in-silico.ch> | 2020-11-07 00:37:32 +0100 |
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| committer | Christoph Helma <helma@in-silico.ch> | 2020-11-07 00:37:32 +0100 |
| commit | ce8db67ce38095e06d2131eced2acfc219661580 (patch) | |
| tree | 1f67c72a123ba7aeed4d71098cd032f1de12818f | |
| parent | 2936efd649e6494220b7474f8e79761d5fa84136 (diff) | |
git urls fixed
| -rw-r--r-- | mutagenicity.md | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/mutagenicity.md b/mutagenicity.md index 4d0a602..4a7e4b3 100644 --- a/mutagenicity.md +++ b/mutagenicity.md @@ -160,7 +160,7 @@ structures. Source code for all data download, extraction and merge operations is publicly available from the git repository <https://git.in-silico.ch/mutagenicity-paper> under a GPL3 License. The new combined dataset can be found at -<https://git.in-silico.ch/mutagenicity-paper/data/mutagenicity.csv>. +<https://git.in-silico.ch/mutagenicity-paper/tree/data/mutagenicity.csv>. ### Pyrrolizidine alkaloid (PA) dataset @@ -428,7 +428,7 @@ potential of the PA dataset. #### Availability -R scripts for these experiments can be found in https://git.in-silico.ch/mutagenicity-paper/scripts/R. +R scripts for these experiments can be found in https://git.in-silico.ch/mutagenicity-paper/tree/scripts/R. ### Tensorflow models @@ -471,7 +471,7 @@ Validation #### Availability -Jupyter notebooks for these experiments can be found in https://git.in-silico.ch/mutagenicity-paper/scripts/tensorflow. +Jupyter notebooks for these experiments can be found in https://git.in-silico.ch/mutagenicity-paper/tree/scripts/tensorflow. Results ======= @@ -498,9 +498,9 @@ and @tbl:tensorflow Tensorflow results. {#fig:roc} Confusion matrices for all models are available from the git repository -https://git.in-silico.ch/mutagenicity-paper/10-fold-crossvalidations/confusion-matrices/, +https://git.in-silico.ch/mutagenicity-paper/tree/10-fold-crossvalidations/confusion-matrices/, individual predictions can be found in -https://git.in-silico.ch/mutagenicity-paper/10-fold-crossvalidations/predictions/. +https://git.in-silico.ch/mutagenicity-paper/tree/10-fold-crossvalidations/predictions/. The most accurate crossvalidation predictions have been obtained with standard `lazar` models using MolPrint2D descriptors ({{cv.lazar-high-confidence.acc}} @@ -516,7 +516,7 @@ Pyrrolizidine alkaloid mutagenicity predictions Mutagenicity predictions from all investigated models for 602 pyrrolizidine alkaloids (PAs) are shown in Table 4. A CSV table with all predictions can be -downloaded from https://git.in-silico.ch/mutagenicity-paper/tables/pa-table.csv +downloaded from https://git.in-silico.ch/mutagenicity-paper/tree/tables/pa-table.csv **TODO** **Verena und Philipp** Koennt Ihr bitte stichprobenweise die Tabelle ueberpruefen @@ -555,7 +555,7 @@ different sources (@Kazius2005, @Hansen2009, @EFSA2016). It contains 8309 unique chemical structures, which is according to our knowledge the largest public mutagenicity dataset presently available. The new training data can be downloaded from -<https://git.in-silico.ch/mutagenicity-paper/data/mutagenicity.csv>. +<https://git.in-silico.ch/mutagenicity-paper/tree/data/mutagenicity.csv>. Model performance ----------------- |