Bioinformatics Student | Computational Biology Researcher | AI for Precision Medicine
BS Bioinformatics student at the University of Agriculture Faisalabad (UAF), working at the intersection of machine learning, transcriptomics, and computational biology to solve biomedical problems.
My current research focuses on AI-driven biomarker discovery, cancer genomics, survival analysis, and precision oncology.
- Cancer Genomics & Transcriptomics
- Machine Learning for Biomedical Data
- Prognostic Biomarker Discovery
- Survival Analysis & Clinical Data Science
- Structural Bioinformatics & Drug Repurposing
- AI Applications in Precision Medicine
Programming:
Python • R • Bash • SQL
Machine Learning & AI:
Scikit-learn • XGBoost • TensorFlow • PyTorch • SHAP
Bioinformatics:
Biopython • BLAST • NCBI • UniProt • GEO • AlphaFold/ColabFold • PyMOL • RasMol
Data Analysis:
Pandas • NumPy • Matplotlib • Statistical Modeling
Machine learning-derived prognostic biomarker validation in the METABRIC breast cancer cohort (n=1,608).
This project evaluates whether ML-nominated biomarkers provide prognostic information beyond established PAM50 molecular subtypes.
Methods:
- Multivariate Cox proportional hazards regression
- Kaplan-Meier survival analysis
- Clinical covariate adjustment
- Prognostic model comparison
- Biomarker validation
Key findings:
- CDCA5 and CMC2 demonstrated independent prognostic value beyond PAM50 subtype
- Improved prognostic performance compared with clinical-only models
(Manuscript prepared for journal submission)
Machine learning pipeline for molecular classification of breast cancer subtypes using GSE45827 microarray gene expression data.
Classification:
- Basal
- HER2
- Luminal A
- Luminal B
Methods:
- Transcriptomic preprocessing
- Feature selection
- XGBoost classification
- Stratified cross-validation
- SHAP-based explainability
- GO/KEGG pathway enrichment
- Reactome (41 significant pathways, FDR<0.05)
Computational protein structure prediction and analysis using AlphaFold2/ColabFold.
Features:
- Protein structure prediction
- pLDDT confidence analysis
- PAE interpretation
- Structural visualization using PyMOL
Currently exploring:
- Structural bioinformatics
- Network pharmacology
- Protein-ligand interaction analysis
- AI-assisted drug discovery
- Develop reproducible computational biology pipelines
- Apply AI for biomedical discovery
- Pursue graduate research opportunities in Bioinformatics, Computational Biology, and AI
💻 GitHub: https://github.com/Zohaib-Bioinfo
🔗 LinkedIn: https://linkedin.com/in/zohaib-bioinfo
"Building computational approaches to understand biology through data, algorithms, and AI."