% % % % % % CODE FOR "In silico learning of tumor evolution through mutational time series" % % % % % % by Noam Auslander, Yuri I. Wolf, Eugene V. Koonin % % % % % % System requirements: %%% 1. MATLAB has to be installed. %%% 2. The following packages should be installed: %% a. Deep Learning Toolbox %% b. Statistics and Machine Learning Toolbox %%% Version checked on: MATLAB2018a,MATLAB2018b %%% OS tested: mac OS 10.12.6 and 10.10.5, NIH HPC linux cluster % % % % % % This code has four parts % % % 1. PART1_PREDICT_LOAD - predict mutational load from a time series of mutations % % % 2. PART2_PREDICT_SEQ - predict the next mutation in the time-sequence % % % 3. PART3_CONSTRUCT_DATA - construction of simulated mutational data % % % 4. PART4_PREDICT_INTERACTIONS - predicting occurrence of mutations from % the binary sequence of major drivers, validation of interactions and % survival analysis % % % each part contains a separate, detailed README file % % %