Disease Agnostic Pathway Subtyping Framework
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