Learning curves won’t tell you exactly how many samples to collect, but they will tell you whether collecting more is worth it at all. In domains where each sample costs real money, that’s the question that actually matters.
On the counterintuitive finding that randomly selecting features from high-dimensional genomic data often matches the performance of careful feature engineering and why that makes mathematical sense.
In biotech, the most common dashboard feature request is a filterable, sortable table. The fastest solution is usually a CSV download and the spreadsheet software people already know.
A hackathon project that chains LLM calls with a product ingredient database and Wikipedia to generate skincare formulations — ingredients, assembly protocols, and allergen warnings.
What makes a good bioinformatics pipeline? A short, non-technical take on the things that matter — from user requirements to reproducibility to knowing when good enough is good enough.