An accurate pipeline for predicting the pathogenicity of human exon structural variants
-
Updated
Jul 30, 2023 - Perl
An accurate pipeline for predicting the pathogenicity of human exon structural variants
AI-powered variant pathogenicity prediction for cancer genes using Evo2-7B evolutionary foundation model. Combines deep learning, VEP annotation, gnomAD frequencies, and explainable AI for precision oncology. Built with Next.js + Modal + PostgreSQL.
Supplementary Data for Pillai et al., 2026.
DevScore: predicting variant pathogenicity through developmental gene expression timing
A CLI-operated bioinformatics platform for gene variant pathogenicity screening and computational gene therapy candidate identification. Integrates an AI interpreter to generate biologically grounded hypotheses based on PRISM result data, and proposes experimental follow-ups. Includes project system file navigation + accession for workflow ease.
To associate your repository with the pathogenicity-prediction topic, visit your repo's landing page and select "manage topics."