Supporting data for "Reliable interpretability of biology-inspired deep neural networks"

<p><strong>Contents</strong></p> <p><em>data.tgz</em> contains all data necessary for reproducing the analysis in the manuscript. After cloning the GitHub repository, extract the contents of this file into folder <em>data</em>. The archive contains the following subfolders:</p> <ul> <li><em>dtox</em><br> DTox results, one subfolder per seed <ul> <li><em>module_relevance.tsv</em>: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): compound identifier</li> <li>remaining columns: node identifiers (UniProt and Reactome IDs)</li> </ul> </li> <li><em>test_labels.csv</em>: predictions for the test set, with two columns: <ul> <li>truth: true label (0 or 1)</li> <li>predicted: predicted label (decimal number between 0 and 1)<br> &nbsp;</li> </ul> </li> </ul> </li> <li><em>mskimpact_[cancer type]_[experiment]</em><br> P-NET results using the MSK-IMPACT 2017 dataset, one subfolder per seed<br> [cancer type] is one of bc (breast cancer), cc (colorectal cancer), nsclc (non-small cell lung cancer), or pc (prostate cancer)<br> [experiment] is one of original (original setup) and shuffled (shuffled labels)<br> &nbsp;</li> <li><em>pnet_[experiment]</em><br> P-NET results using the original (prostate cancer) dataset, one subfolder per seed<br> [experiment] is one of deterministic (deterministic input data), original (original setup), and shuffled (shuffled labels) <ul> <li><em>node_importance.csv</em>: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): node name</li> <li>coef: original node importance scores</li> <li>coef_graph: indegree plus outdegree of node</li> <li>coef_combined: adjusted node importance score (= coef / coef_graph if coef_graph &gt; mean(coef_graph) + 5 sd(coef_graph) in the respective layer)</li> <li>coef_combined_zscore: scaled coef_combined</li> <li>coef_combined2: z(z(coef_graph) - z(coef))</li> <li>layer: layer of the node</li> </ul> </li> <li><em>predictions_test.csv</em>: predictions for the test set, with the following columns: <ul> <li>(first, unnamed): sample name</li> <li>pred: predicted class (unfortunately, encoded by a double 1.0 or 0.0)</li> <li>pred_scores: probability of the predicted class</li> <li>y: true class (encoded as integer 1 or 0)</li> </ul> </li> <li><em>predictions_train.csv</em>: predictions for the training set (same columns as above)</li> <li><em>link_weights_[layer].csv</em>: only in subfolder 234_20080808; matrices with edge weights</li> </ul> </li> </ul> <p>&nbsp;</p> <p><strong>Changelog</strong></p> <p><em>v1.1.0&nbsp; &ndash; 2023-06-28</em></p> <ul> <li>added DTox results</li> <li>added results of P-NET experiments with MSK-IMPACT 2017 dataset</li> </ul> <p><em>v1.0.0 &ndash; 2023-03-22</em></p> <ul> <li>initial release</li> </ul>