Senior Research Fellow (Ph.D.) · Computational Biology & Bioinformatics Lab
BRIC-Institute of Life Sciences, Bhubaneswar
Projects · Research · Skills · Activity · Portfolio ↗
Whole-genome sequencing and metagenomics workflows for short, long, and hybrid reads.
Otitis-media-associated genes, methylation study targets, and microRNA interactions.
Bioinformatica ↗ — Bioinformatics tools, databases, and tutorials.
Heterogeneous graph neural networks for drug synergy prediction.
Explore the research
- Graph constellations: heterogeneous graph neural networks connect different biological entities and pass information along their relationships.
- Drug synergy: learning across drug combinations and biological context supports synergy prediction. The animated matrix illustrates this research direction without displaying measured scores.
- Mathematical models: response curves illustrate how a system can evolve over time. These trajectories are schematic, not fitted experimental data.
Skills & tools
| Area | Skills |
|---|---|
| Languages & interfaces | Python · R · Rust · Perl · Java · JavaScript · TypeScript · HTML · React |
| Machine learning & APIs | PyTorch · PyTorch Geometric · DGL · Scikit-learn · FastAPI |
| Cloud & services | Nginx · Keycloak · AWS · Firebase |
| Data & storage | PostgreSQL · SQL · MinIO · Redis |
| Workflows & computing | Snakemake · Docker · Linux · HPC · Slurm · Git · GitHub |
| Bioinformatics | Genome assembly & annotation · DESeq · edgeR · STAR · Omics integration · Network biology |
| Modelling | Graph neural networks · Metabolic networks · Protein interactions · Mathematical modelling · Molecular docking & dynamics · QSAR · Cheminformatics |
| Design & writing | LaTeX · Illustrator · InDesign · Photoshop · After Effects · Audition · Premiere Pro |
Repository activity
