I build AI-assisted research tools for genomics, polygenic risk scoring, biomedical literature mining, variant interpretation, genotype–phenotype prediction, and scientific workflow automation.
I am a PhD Candidate in Computational Biology at The University of Queensland, working at the interface of artificial intelligence, genomics, bioinformatics, statistical genetics, biomedical NLP, and translational discovery.
My research focuses on developing reproducible computational frameworks for:
- Genotype–phenotype prediction
- Polygenic risk score benchmarking
- AI-driven biomedical literature mining
- Variant interpretation and evidence extraction
- Phenotype-associated gene discovery
- Genetics-informed drug repurposing
- Multimodal biomedical data integration
- Research software and workflow automation
I enjoy building practical tools that turn complex biological, clinical, and literature-scale datasets into usable research outputs.
AI + Genomics
├── Polygenic Risk Scores and genomic prediction
├── Genotype–phenotype modelling
├── Biomedical NLP and LLM-based evidence extraction
├── Variant interpretation and literature mining
├── Drug repurposing and target prioritisation
├── Heritability estimation and benchmarking
├── Reproducible bioinformatics pipelines
└── Research software, dashboards, and web tools
| Project | Area | Description |
|---|---|---|
| PRSGPT | LLMs / PRS / Bioinformatics | Domain-adapted large language model workflow for polygenic risk score guidance and bioinformatics question answering. |
| BioStarsGPT | Biomedical NLP / LLMs | Fine-tuned LLM system for bioinformatics Q&A, scientific knowledge extraction, and specialised research support. |
| LitVarAI | Biomedical Literature Mining | AI-assisted pipeline for biomedical literature discovery, variant extraction, evidence annotation, and ClinVar-oriented validation. |
| PRSTools | Polygenic Risk Scores | Benchmarking and implementation-aware evaluation framework for polygenic risk score tools and genomic prediction workflows. |
| GWASPoker | GWAS / PRS QC | Automated tool for parsing, validating, harmonising, and assessing GWAS summary statistics before PRS analysis. |
| EFGPP | Genotype–Phenotype Prediction | Exploratory framework integrating PRS, phenotype-level signals, cross-trait features, and machine learning for genomic prediction. |
| G2DR | Drug Repurposing | Genotype-first framework for genetics-informed target prioritisation, drug repurposing, and translational evidence synthesis. |
| JobsDownloaderAndRanker | Applied AI / NLP | Local-first job discovery and resume-ranking system using semantic similarity, skill matching, and interactive dashboards. |
| PhenotypeToGeneDownloaderR | Disease-Gene Evidence | R pipeline for phenotype-to-gene evidence collection, database comparison, validation, and downstream translational analysis. |
| Automating Literature Review | AI Research Automation | AI-assisted system for literature retrieval, summarisation, evidence ranking, and biomedical research synthesis. |
| Platform | Description |
|---|---|
| MuhammadLab Tools | Browser-based tools for research, documents, images, data processing, learning, AI, security, forensics, and genetic file-format utilities. |
| Muhammad Lab | Educational and research technology platform covering AI, programming, cybersecurity, bioinformatics, and research workflows. |
| ResearchMate | Research-support platform for discovering supervisors, collaborators, and academic profiles using structured search and matching workflows. |
| SwiftConvertor | Web-based conversion platform for documents, images, PDFs, text, and productivity utilities. |
mindmap
root((My Research))
AI for Genomics
Genotype-Phenotype Prediction
Deep Learning
Multimodal Data Integration
Polygenic Risk Scores
PRS Benchmarking
GWAS Summary Statistics
Heritability Estimation
Biomedical NLP
Literature Mining
Variant Extraction
LLM Fine-Tuning
Translational Genomics
Drug Repurposing
Target Prioritisation
Disease-Gene Evidence
Research Software
Python Pipelines
R Workflows
Dashboards
Web Tools
| Degree | Institution | Focus |
|---|---|---|
| PhD in Computational Biology | The University of Queensland, Australia | AI-driven genomics, genotype–phenotype prediction, PRS benchmarking, translational genomics |
| MSc in Computer Science, Artificial Intelligence | Khalifa University, UAE | Genotype–phenotype prediction using artificial intelligence algorithms |
| BSc in Computer and Information Sciences | PIEAS, Pakistan | Computer science, AI, software engineering, blockchain, and applied computing |
I also teach and develop learning resources across information technology, programming, cybersecurity, digital forensics, machine learning, and research computing.
Current and previous teaching areas include:
- Python programming
- Machine learning applications
- Cybersecurity
- Digital forensics
- Information networks
- Advanced programming
- IT project management
- Professional practice in IT
Teaching resources: Muhammad Muneeb Teaching Resources
I am interested in building AI systems that can help researchers move from:
genetic data
↓
biological interpretation
↓
literature evidence
↓
disease-gene understanding
↓
therapeutic hypothesis generation
↓
reproducible research software
My long-term goal is to develop practical, transparent, and reproducible AI tools that support biomedical discovery and translational genomics.
Building AI-assisted tools for genomics, biomedical discovery, and reproducible research.