🧬🔮 Single cell foundation models have been a recent hot topic in bio-ML! A few of the recent methods and some thoughts 🧬🔮 1) Geneformer 2) scGPT 3) scFoundation 4) Exceiver

The recent breakthroughs from @nablabio & @chaidiscovery emphasize a split in early biotech strategy. For the specific range of problems that antibodies address, making the binder, is becoming trivial. This forces a choice between 'fast but competitive' and 'AI intractable'. 🧵

This is a major reason I joined @transcriptabio We've proved our platform in rare disease – an area that uniquely allows you to: 1) realize the mission of helping people, immediately 2) receive the gold-standard of clinical feedback, immediately 📄:

Most current drug discovery efforts is structure-based eg. create small molecules or antibodies that best binds X. However, a drug may not drive its efficacy from its strongest binder. Taking a step away from structure-paradigm, we reason that if a CRISPR knockout of a gene

“this discussion on the challenges of evaluating a Foundation model is more interesting than the challenge itself.” Agreed!

We're excited to present LeaVS, a method to scale up learning for protein function models. It is based on the co-design of wet lab experiments and in silico training.

Arc is hiring a unique role to lead the Virtual Cell Challenge. In its first year the Challenge has already attracted participation from thousands of top bio AI researchers and support from sponsors like NVIDIA. We need someone to help us make this annual competition historically

Massive, clean Pertub-seq dataset. 8M cells, 2 cell types, deeply sequenced. 🧬🪩 👏

Awesome 👏🏼👏🏼

https://t.co/BW98jZybL3

👏👏 last year, I used some metrics from Logan to help me analyze the volume and growth rate of genomics data you can find "Scaling biology: genomics" in the reply

*using the ctrl pop as a ref biases perturbation effects to be poorly distinguishable* a new study by @mariabrbic's lab reaches the same conclusion as the @ShiftBioscience study I highlighted recently 👏 ...with an nice fwd comment on model "utility" 😎 https://t.co/jTqs8oM1Ti

The biggest challenge for AI in biology isn't just models, it's the data used to train them. Standard biological data isn't built for AI. To unlock generative AI for drug discovery, we must rethink how we generate and capture data. 1/

I highlight a recent paper from @BoWang87 and colleagues at @ShiftBioscience that mark new progress towards the larger goal.

Second, the field has been rallying to describe metrics that provide high signal and prediction "utility". Good measurements, confidence metrics, and directly actionable outcomes are crucial for utility. 3/n

First of all, it's important to delineate between cis- and trans- gene regulatory modeling. 2/n

What is the state of research on the emerging grand challenge of virtual cell modeling? 1/n https://t.co/lOm7d8nd0g

I highlight a recent paper from @BoWang87 and colleagues at @ShiftBioscience that mark new progress towards the larger goal.

Second, the field has been rallying to describe metrics that provide high signal and prediction "utility". Good measurements, confidence metrics, and directly actionable outcomes are crucial for utility.

First of all, it's important to delineate between cis- and trans- gene regulatory modeling. 2/n

Further, some simple changes may greatly improve the many available methods, particularly when compared against these new well-calibrated baselines. There is much more progress to follow from this. Great job @BoWang87 and the @shiftbioscience team!