Projects

Peptide Design using RNN

LSTM-Based Deep Learning Model for the Discovery of Antimicrobial Peptides Targeting Mycobacterium tuberculosis

Mix infection analysis through machine learning

Mixed infections in genotypic drug-resistant Mycobacterium tuberculosis

GNN for positive selection detection

Decoding Positive Selection in Mycobacterium tuberculosis with Phylogeny-Guided Graph Attention Models

Philippines TB transmission report

Whole genome sequencing analysis of Mycobacterium tuberculosis reveals circulating strain types and drug-resistance mutations in the Philippines

TOAST Amplicon Designer Tool

A novel tool for designing targeted gene amplicons and an optimised set of primers for high-throughput sequencing in tuberculosis genomic studies

Treatment outcome prediction for Clinical Records

A multi-stage machine learning framework for stepwise prediction of tuberculosis treatment outcomes - Integrating gradient boosted decision trees and feature-level analysis for clinical decision support