Tag Archives: Machine Learning

Beyond the 2D Bounding Box: Why Bioimage Deep Learning Is a Completely Different Beast and How BiaPy Tames It

By | August 24, 2026
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https://github.com/lynchaos/biapy-hub https://lynchaos-biapy-hub-app-hlcmfj.streamlit.app/ If you have spent your machine learning career optimizing ResNets or YOLO on ImageNet and COCO, here is an inconvenient truth: standard computer vision has spoiled you. In standard computer vision, a batch is usually an RGB triplet where . The pixels are isotropic, the spatial coordinates are Euclidean, the lighting is roughly… Read More: Beyond the 2D Bounding Box: Why Bioimage Deep Learning Is… »

I turned a Raspberry Pi into a tiny weather intelligence powerhouse

By | August 16, 2026
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A Raspberry Pi Zero 2 W paired with a Sense HAT V2 runs a fully self-contained, edge-native machine learning weather station with no cloud, GPU, or heavy ML frameworks. The system uses pure NumPy implementations of Recursive Least Squares, Kalman filtering, conformal prediction, and drift detection, staying under 150 MB RAM. The station continuously updates… Read More: I turned a Raspberry Pi into a tiny weather intelligence… »

A Tonne of Duck in One Run: What Parima and Vow Actually Proved

By | July 18, 2026
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A large white duck stands in a misty wetland at sunrise near a stone etched with "VOW".

The headlines led with a 22,000-litre tank and a 99% cost drop. Here is a bioreactor engineer’s read on what Parima and Vow actually proved, why the media breakthrough matters more than the vessel, and the quiet shift that turned cultivated meat into a fermentation problem.

From Bioreactors to Brier Scores: Building Model90, a Football Prediction Engine

By | July 5, 2026
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What happens when a bioreactor engineer points his state-estimation toolkit at football. Model90 is a statistical forecasting engine for the 2026 World Cup and the major leagues, built on Dixon-Coles Poisson, Elo, xG and a calibrated meta-model. An honest look at how it works and what its Brier score really means.

From Lab Bench to Browser: A Hybrid Digital Twin for CHO Cell Culture

By | May 30, 2026
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CHO Digital Twin

I rebuilt two published CHO cell-culture papers — a hybrid ODE + machine-learning growth model, and a genome-scale metabolic reduction pipeline — as an interactive digital twin that runs in the browser. Here’s how it works, what’s under the hood, and an honest take on what it’s good for. No hosted version yet; the code is on GitHub and a live instance is coming soon.

Building ML Tools Scientists Will Actually Use

By | January 25, 2026
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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 »

Case Study: Predicting Trastuzumab Developability

By | January 25, 2026
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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 »

How I Built a Machine Learning Tool to Predict Drug Manufacturing Failures

By | January 25, 2026
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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… »