Explainable deep learning for stratified medicine in inflammatory bowel disease

Moving from a one-size-fits-all to an individual approach in precision medicine requires a deeper understanding of disease molecular mechanisms. Especially in heterogeneous complex diseases such as inflammatory bowel disease (IBD), better molecular stratification will help select the correct therapy. For this, we build end-to-end biologically sparsified neural network architectures for IBD subtyping based on whole exome sequence representations with gene-level and variant-level resolution. By moving beyond univariate methods, we capitalize on the model's ability to extract complex molecular patterns to improve prediction. Model interpretation identifies the most predictive pathways, genes, and variants, uncovering important intestinal barrier, immunological, and microbiome factors.