I'm always open to new opportunities to learn and contribute. Let's connect if you're working on:
I'm Muhammad Ekramah, a passionate Bioinformatics student at Quaid-i-Azam University (QAU) in Islamabad, Pakistan. My academic and research journey is focused on integrating computational biology with machine learning to address global health challenges, particularly in the field of Antimicrobial Resistance (AMR).
My expertise lies in Next-Generation Sequencing (NGS) data analysis, resistome profiling, and applying AI-driven insights to genomics. I have hands-on experience with NGS pipelines and AMR surveillance datasets, which I aim to leverage to contribute impactful solutions in precision medicine.
I'm currently authoring a narrative review on metallo-Ξ²-lactamases (NDM, VIM) to explore resistance mechanisms and therapeutic strategies. My goal for 2025 is to publish 2+ papers in AMR bioinformatics and AI-driven biology.
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Bioinformatics:
- NGS Data Analysis (DNA-seq, RNA-seq, Metagenomics)
- Resistome & AMR Profiling
- Single-cell & Transcriptomics (Bioconductor, Galaxy, R)
- Large-scale Genomics Pipelines
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Programming & Data Science:
- Python, R, SQL
- Pandas, NumPy, scikit-learn, TensorFlow
- Machine Learning for Genomics & Healthcare
- Predictive Modeling & AI-driven Insights
| Project | Description | Tech Stack |
|---|---|---|
| AMR Resistome Profiling π¦ | Analyzed patient & wastewater datasets to identify resistance genes. | Python, R, AMR package |
| RNA-seq Workflow 𧬠| Developed a differential expression analysis pipeline for transcriptomics. | Bioconductor, Galaxy |
| ML for Genomics π€ | Built predictive models for resistance patterns using ML algorithms. | scikit-learn, TensorFlow |
