The reason doing #ML in #biology is much harder than doing ML on text is that "ground truth" is very different. In the post below I'll show you the first 5 things we check when curating one single data point to teach the right idea to the #AI. #compbio #virtualcell blog.turbine.ai/p/ic50-is-a-...

Why can #AI work in #biology? It's not about hidden shortcuts to do raw physics. I think it's because we are learning how evolution was shaping us over all this time to survive. blog.turbine.ai/p/why-can-we... So maybe we should move on from individual proteins to learning relations of processes.

Single-cell foundation models, trained on large-scale scRNA-seq datasets, are increasingly used for post-perturbation RNA-seq prediction. But how do they actually perform? Our new paper from @turbine-ai.bsky.social is now out in BMC Genomics. bmcgenomics.biomedcentral.com/articles/10.... 1/5

Turns out the way we usually pre-train foundational cell models adds very little information to the system - definitely not enough to make drug effect predictions work. Not what I've expected. #virtualcells #foundationalmodels #compbio blog.turbine.ai/p/pretrainin...

The 'Google Co-Scientist' claims to be 'Accelerating scientific breakthroughs with an AI co-scientist' But does it? A closer look at its scientific validation as currently reported Please share and let me know if you have any comments, cheers! Andreas www.drugdiscovery.net/2025/02/20/t...

also check out blog.turbine.ai/archive :)

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