Beyond projects, I believe it's important to discuss the concepts and challenges in our field. Here I share tutorials, paper reviews, and my thoughts on the future of computational biology.
A Step-by-Step Guide to Building Your First Cancer Classifier
Ever wondered how to apply machine learning to a real biological problem? In this post, I walk through the complete process of building a tumor classifier, from data cleaning in Pandas to model training with Scikit-learn.
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The Role of Genomics in Combating Antimicrobial Resistance
Antimicrobial resistance is a global crisis. I review a recent groundbreaking paper and discuss how whole-genome sequencing is changing the game for AMR surveillance and outbreak investigation.
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Why Reproducibility is the Cornerstone of Computational Biology
In a field driven by code, how can we ensure our results are reliable? I explore the importance of tools like Git, Docker, and RMarkdown in creating transparent and reproducible scientific research.
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