I'm a computational biologist passionate about transforming complex biological data into meaningful insights. My work spans across multiple 'omics' domains, with particular expertise in:

• 📊 RNA-seq analysis and transcriptomics
• 🤖 Machine learning applications in biology
• 🧬 Multi-omics integration and systems biology
• 📈 Data visualization and reproducible research

Currently focused on developing novel computational approaches to understand gene regulation, disease mechanisms, and therapeutic targets through large-scale data analysis.

Comprehensive ML tutorials for biological data

• Educational resource for applying ML to bioinformatics
• Covers supervised/unsupervised learning with biological examples
• Jupyter notebooks with step-by-step explanations

Python Scikit-learn Jupyter Machine Learning Bioinformatics

End-to-end transcriptomics analysis workflow

• Complete pipeline from raw reads to biological insights
• Differential expression and pathway enrichment analysis
• Reproducible research with documented methodologies

R Bioconductor DESeq2 GSEA RNA-seq Transcriptomics

Systems biology approach to disease understanding

• Integration of transcriptomics, proteomics, and metabolomics
• Network-based analysis and biomarker identification
• Machine learning for predictive modeling

Python R Network Analysis Systems Biology Multi-omics

research_focus = {
    "primary": [
        "RNA-seq analysis and transcriptomics",
        "Machine learning applications in biology",
        "Multi-omics data integration",
        "Clinical trial data analysis",
        "Biomarker discovery",
        "Regulatory bioinformatics"
    ],
    "emerging": [
        "Graph neural networks for biological networks",
        "Single-cell omics analysis",
        "Computational drug discovery",
        "Real-world evidence analysis",
        "Digital therapeutics",
        "Precision medicine algorithms"
    ],
    "methodologies": [
        "Differential expression analysis",
        "Pathway enrichment and GSEA",
        "Dimensionality reduction techniques",
        "Network-based analysis",
        "Reproducible research workflows"
    ]
}

• 🔬 Research: Developing GNN models for bacterial growth prediction
• 📚 Education: Creating comprehensive bioinformatics tutorials
• 🤝 Collaboration: Open to new roles, partnerships and consulting
• 🌟 Open Source: Contributing to bioinformatics tool development

I'm always interested in discussing:

• Collaborative research, new roles opportunities
• Bioinformatics consulting projects
• Educational initiatives in computational biology
• Open source contributions to the community