A reproducible, cloud-scale reanalysis of five peer-reviewed studies on cellular aging, reprogramming, and rejuvenation, and an honest account of what survived contact with a shared pipeline.
The Gap Between Models and Tools I’ve seen a lot of impressive ML models in biopharma that never get used. Not because the science is wrong, but because the tool doesn’t fit into anyone’s workflow. The model might be published in Nature Methods with beautiful receiver operating characteristic curves, but if a discovery scientist can’t… Read More: Building ML Tools Scientists Will Actually Use »
Why Trastuzumab Is the Perfect Test Case When I built my antibody developability predictor, I knew I needed to validate it against a molecule where we actually know the manufacturing story. Trastuzumab (Herceptin) was the obvious choice. It’s one of the most successful therapeutic antibodies ever made, with decades of manufacturing data behind it. More… Read More: Case Study: Predicting Trastuzumab Developability »
A bioprocess engineer’s journey into machine learning and why the pharmaceutical industry desperately needs this bridge When I tell people I work in bioprocess engineering, I usually get blank stares. When I explain that I help manufacture proteins in giant tanks for therapeutic use, the response is often: “Oh, like brewing beer?” Not quite. But… Read More: How I Built a Machine Learning Tool to Predict Drug… »