Part 4 of 5. Right now SimurgArray lives in a private repository on my own Git server. It has a license, a changelog, a user guide, wheels, a Docker image and a version number that just became 2.0.0. In other words, it looks public. It is not, yet. Publishing code is easy. Publishing it responsibly… Read More: SimurgArray going public: what it takes, and a calendar »
Part 3 of 5. “Benefit to humanity” is a big phrase for a tool that mostly reads binary files. I will try to keep this post the right size. SimurgArray will not cure anything. What it can do is lower a few barriers that currently decide who gets to do genetics research and how much… Read More: SimurgArray: What good could it do? »
Part 2 of 5. Whenever someone tells me they are writing their own genotype caller, my first instinct is to ask whether they have looked around. So it is only fair that I answer the same question about myself. I did look around. There is a lot of good work out there. Some of it… Read More: Are there SimurgArray alternatives? Yes. Why build it anyway? »
Part 1 of 5. Every genotyping array tells a small story before it tells you anything about DNA. A glass slide goes into a scanner, the scanner writes two IDAT files per sample (one red, one green), and a few hundred thousand bead intensities wait for someone to turn them into genotypes. That someone is… Read More: Why SimurgArray should exist? »
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… »
Repository: github.com/lynchaos/generative-microscopy Why this sketch I wanted to find out whether I could make something that genuinely looked like microscopy footage. Not cells bouncing around a canvas, but Saccharomyces cerevisiae behaving the way it actually behaves under a coverslip, seen through real glass and real light. The sketch began life as a generic n-body attraction… Read More: Building a fake microscope: simulating a yeast wet mount »
Single-cell RNA sequencing is one of those techniques that generates a huge amount of data and then demands a huge amount of effort to interpret it. You run the sequencer, get a matrix of 20,000 genes by several thousand cells, and then spend the next several days (or weeks) figuring out what’s in there. I… Read More: I Built a Single-Cell RNA-seq Pipeline That Writes Its Own… »
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 »