Disease Agnostic Pathway Subtyping Framework

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Authors: Rohit Chauhan
Cited by: 0

Abstract

A disease-agnostic framework for pathway-based molecular subtype discovery in genetically heterogeneous conditions. Features:- VCF parsing with gene/consequence annotation- Pathway-level burden scoring (GMT format)- GMM-based clustering with BIC model selection- Three validation gates: label shuffle, random gene sets, bootstrap stability- CLI and Python API- Support for autism, schizophrenia, epilepsy, and intellectual disability Installation: pip install pathway-subtyping Documentation: https://github.com/topmist-admin/pathway-subtyping-framework

Fields of Study & Subjects

Bioinformatics and Genomic Networks Genomics and Rare Diseases Genetic Associations and Epidemiology

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
31 January 2026
Language
EN