Machine learning and metabolic modeling-based identification of hypoxia-driven metabolic signatures in pediatric cancers

The repository contains codes for Addition of RS and basal essential media to pediatric cancer GEMs grrules_create_01.m mem_constraint_02.m mem_sink_03.m rs_merge_04.m loop_check_05.m Generation of parsimonious flux data and generation of machine learning features from flux data pfba_06.ipynb feature_generation_07.ipynb Machine learning and feature interpretation using SHAP ML_analysis_08.ipynb Additional files RS_demands.py - to add compartmental and total demand reactions to the reactive species loopcheck.m - to check the presence of thermodynamically infeasible cycles memhuman.m - details about uptake rates of nutrients from basal essential media