diff --git a/README.md b/README.md index 21e732c8a21f62789e10acf361db734444b47dc2..1aac081041db63cedd4269ba3cbda7cf2807a133 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ Make sure you have the following Python packages installed: * sk-learn * MatplotLib * Weblogo - +* Bokeh # Getting started @@ -34,9 +34,16 @@ After creating the TFRecords, training set and test set, you can run 'train.py'. # Interpreting the results  +After evaluating the test set, the file interpretation.html will be created. In this file you will find two interactive plots: + +* An interactive heatmap, which shows which filter pairs contributed to the classifcation of a sample +* A plot showing at which sequence position(s), of the sample selected in the heatmap, the filters found a match that was above the learnt similarity threshold + +Select a sample and filter pair by clicking the corresponding tile in the heatmap. A tooltip with WebLogo's corresponding to a filter pair can be shown by hovering the cursor over a tile. A selected sample and filter pair will be plotted in the sequence position plot; that is, each position (horizontal axis) where a filter is found above the learnt detection threshold is annotated with a triangle (first filter in a pair) or an inverted triangle (second filter in a pair). The colour of a triangle indicates the similarity score between the filter and amino acids at the position the triangle is found. + # References [1] Pan, X. Y., Zhang, Y. N., and Shen, H. B. (2010). Large- scale prediction of human protein-protein interactions from amino acid sequence based on latent topic features. Journal of Proteome Research, -9(10):4992–5001. \ No newline at end of file +9(10):4992–5001.