Enhancing patient stratification and interpretability through class-contrastive and feature attribution techniques

Sharday Olowu, Neil D Lawrence, Soumya Banerjee

Keywords: explainable AI, machine learning, patient stratification, inflammatory bowel disease, Crohn's disease, Gaussian Mixture Modelling, kernelSHAP, gene modules, visual explanation, subtype prediction, personalized medicine, bioinformatics, interpretable models, feature correlation, model-agnostic approach

TL;DR: This study develops explainable ML methods for IBD patient stratification, using Gaussian Mixture Modelling and modified kernelSHAP to classify disease subtypes and provide interpretable predictions.

Abstract: A crucial component of treating genetic disorders is identifying the genes and gene modules that drive disease processes. While Next-Generation Sequencing (NGS) provides rich data for this task, current machine learning approaches often lack explainability and fail to account for gene correlations. We develop a comprehensive framework of machine learning techniques for explainable patient stratification in inflammatory bowel disease, focusing on Crohn's disease (CD) subtypes: CD with deep ulcer, CD without deep ulcer and IBD-controls. Our approach combines Gaussian Mixture Modelling for patient stratification, a modified kernelSHAP algorithm accounting for gene co-expression, systematic identification of gene modules, and class-contrastive analysis for explaining individual patient phenotypes. This framework confirms known disease-associated genes while unveiling novel genetic factors potentially underlying CD heterogeneity. Gene Ontology enrichment analysis validates the biological relevance of identified gene modules and associated pathways. Our methods offer a versatile toolkit for analysing high-dimensional, correlated biological data across diverse disease contexts.

Track: Main track

Submitted Paper: Yes

Published Paper: No

Submission Number: 18