Profiling tool for Mycobacterium tuberculosis to detect ressistance and strain type from WGS data

Minor Variant Calling and Phasing Tools

Drug Response Estimation from single-cell Expression Profiles

Genome classificator using machine learning and SNP markers.

Snakemake pipelines to run the analysis for the Illumina vs. Nanopore comparison.

Generate a rich feature space describing all possible mutations in a given protein sequence (+ structure)

A comprehensive pipeline for HIV drug resistance identification, variant evaluation, and surveillance.

Multipurpose tool for targeted variant calling of genetic polymorphisms in Mycobacterium Tuberculosis

Data and analysis scripts used in Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations

A deep learning framework for HIV-1 drug resistance prediction using context-aware fusion of sequence and mutation features, with hybrid rule–model evaluation and robustness analysis. The model demonstrates adaptive feature reliance and strong cross-drug generalization.

A method to predict activating, deactivating and resistance mutations in kinases

A GLUE project for hepatitis B virus (HBV).

A GLUE project for Hepatitis C virus (HCV)

This study assessed the spatial genetic variation in P. falciparum populations in Mali

HCV drug resistance extension based on collaboration with PHE.

Clusters phosphoproteomics data based on a combination of the sequence information and abundance changes over conditions.

Examine treatment outcomes, and predict the incidence of Tuberculosis

Flexible and disposable paper and plastic-based gel micropads for nematode handling, imaging and chemical testing

The SNP and gene datasets of M. Tuberculosis for drug resistance prediction.

Machine learning models for predicting antimicrobial resistance in Mycobacterium tuberculosis using genomic features