Will Connell

🧬🔮 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

Great to see this out 👏🏼 plz read for a really big idea Also, I wrote an overview of Variational Synthesis in 2024 open.substack.com/pub/behindbiom…

"We find that intra-complex interactions are largely conserved, whereas inter-complex relationships are extensively rewired, revealing new context-dependent genetic dependencies." 👏 💡rich resource for virtual cell benchmarking to disambig contextual-modeling vs coexpr-modeling

@j0hnparkhill @jermdemo What service did you use? I’d consider publishing my own 🤔

@jermdemo @wilstc I recently got my full seq done, but wouldn't be comfy with it floating around....

I built Scaling Biology 🧬 — a dashboard that live-tracks the volume and growth of key biological data sources across genomics, transcriptomics, and proteomics. The project is open to community contributions, check out the repo linked in footer wconnell.github.io/scaling-bio/

@jermdemo any pointers toward resources that could help capture that stat?

@wilstc i really wish we had more than 5000 truly public human genomes

1/13 Excited to share our (@anna_spiro @ChikinaLab @sara_mostafavi) latest preprint! 🧬💻 Personal Genome Prediction isn't just a downstream task—it’s the ultimate end-to-end benchmark for Variant Effect Prediction. We put the new SOTA AlphaGenome to the test and uncovered a striking "Modality Gap" between gene expression and chromatin accessibility. 📄 Link: biorxiv.org/content/10.648… 🧵👇

Together with Emma Dann, we are thrilled to present a massive new Perturb-seq atlas of 22M primary CD4+ T cells, from 4 donors, across 3 timepoints – the result of a decade-long collaboration between the Marson (@MarsonLab) and Pritchard (@jkpritch) labs. 🧵👇

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 📄: researchsquare.com/article/rs-629…

@RuxandraTeslo @JackScannell13 Nice, I wrote up a similar perspective earlier this year! x.com/wilstc/status/…

You have heard of AI slop in the context of short video creation. But the same principle applies when it comes to improving drug discovery: we absolutely do not need a deluge of new hypotheses; we need better predictive validity (as per @JackScannell13). writingruxandrabio.com/p/what-will-it…

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 mimics a drug's effects across cancer cell lines, that gene is likely the drug's target. This was done in @EytanRuppin in collaboration with @anideshpandelab and @BenDavidLab Using this principle, we integrated drug and crispr profiles from 1000s of drugs to find their context specific targets (different cancers or when known target is not expressed but drug is yet killing cancer cells). We call this tool DeepTarget. We show that this approach outperforms current structure based methods (AF3, RF, Chai) to find drug's target in a genome-wide search, when we had no information on what the target might be. We benchmarked in eight gold-standard drug-target pairs. It took us months to get this benchmarks (we hope this benchmark helps the field) We present two experimentally validated cases and pls see the paper for this (link at the end). An intriguing observation is that we had many cases where we have many small molecules targeting the same gene (eg. EGFR) and we found that small molecules with higher predicted target specificity show greater clinical advancement. Very happy to hear your feedback. Here's the free access link: nature.com/articles/s4169…

@anshulkundaje Metrics are one thing, but there are other major challenges with data. This paper designs improved metrics to highlight bio signal, but my takeaway is actually that most (perturb-seq) datasets have a very low prediction ceiling biorxiv.org/content/10.110…

@wilstc This is a very specific perturbation prediction task. It's not testing foundation models that r expected to solve many diverse tasks. It's very difficult to encapsulate even such focused biologically motivated objectives with a handful of metrics that cannot be gamed.

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

@anshulkundaje Yeah, this task is 1/n you expect such a model to perform well on. Creating community focus on evaluation strategies for this task is a very welcome outcome of the virtual cell challenge!

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 impactful. The job is a mix of product, program, and community management, with collaboration across a wonderful and talented internal team at Arc. The Virtual Cell Challenge is modeled after CASP, which led to AlphaFold and a Nobel prize, and inspired by our board member Nat Friedman's Scroll Prize, which pushed the boundaries of applied machine learning. The person we hire for this role has an opportunity to make the Challenge something really special.

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

Awesome 👏🏼👏🏼

👏👏 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