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 01_grrules_create.m 02_mem_constraint.m 03_mem_sink.m 04_rs_merge.m 05_loop_check.m Generation of parsimonious flux data and generation of machine learning features from flux data 06_pfba.ipynb 07_feature_generation.ipynb Machine learning and feature interpretation using SHAP 08_ML_analysis.ipynb Additional files loopcheck.m - to check the presence of thermodynamically infeasible cycles memhuman.m - details about uptake rates of nutrients from basal essential media RS_demands.py - to add compartmental and total demand reactions to the reactive species