Machine learning and metabolic modeling-based identification of hypoxia-driven metabolic signatures in pediatric cancers
Machine learning and metabolic modeling-based identification of hypoxia-driven metabolic signatures in pediatric cancers
The repository contains codes for 1. 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 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 Basic human tasks performed by GEMs: 10a_Cobra_humantasks.m 10b_humantask_statistic.m 2. Generation of parsimonious flux data and generation of machine learning features from flux data 06_pfba.ipynb 07_feature_generation.ipynb 3. Machine learning and feature interpretation using SHAP 08_ML_analysis.ipynb 4. Systems-level analysis of SHAP important metabolic pathways 09_systems_analysis.ipynb
Pediatric Cancers, systems biology, Constrained-based Metabolic Modeling, Supervised Machine Learning
Pediatric Cancers, systems biology, Constrained-based Metabolic Modeling, Supervised Machine Learning
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