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A multilineage screen identifies actionable synthetic lethal interactions in human cancers. nature.com/articles/s4158…

Possibly my last co-first author paper in academia: Cancer-specific innate and adaptive immune rewiring drives resistance to PD-1 blockade in classic Hodgkin lymphoma. Enjoyed the collaboration at Dana Farber! nature.com/articles/s4146…

Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors go.nature.com/3EitbLm

Good to see moscot finally published in nature! Check out the paper (tinyurl.com/33zuwsep), the research briefing (tinyurl.com/mrvpbkpj), and moscot-tools.org to learn more about it!

as late as last Thursday I had a conversation with a prominent editor convinced AI can only save marginal amounts of time meanwhile Novo Nordisk has gone from a team of 50 drafting clinical reports to just 3 (the 15 weeks to <10 mins surprises me though)

Predicting clinical outcomes of drug combinations from preclinical data is a major challenge Yepeng We know a drug works in the lab. But will it work in patients? 🔬 ➡️ 🏥 This is key for safe and effective therapies and it's one of the hardest challenges in medicine.

This paper introduces the AI co-scientist. It's a multi-agent system designed to aid scientists in generating novel hypotheses and planning experiments. → The AI co-scientist uses specialized agents for different tasks. → These agents include Generation, Reflection, and

A prominent group at the NIH just dropped a cool paper in Nature Cancer - Hallmarks of AI contributions to precision oncology. I am very happy to see that the Generative Tensorial Reinforcement Learning paper we published in 2019 is mentioned in Hallmark #10. Btw. many of the

Bayesian causal survival analysis has never been so easy! Check out Han Ji’s (Brown Biostatistics) paper & package, in press Observational Studies. Convenient syntax, S3 classes & methods, help files, & efficient MCMC via Stan. arxiv.org/pdf/2310.12358 github.com/RuBBiT-hj/caus…

Currently, there's a lot of interest in quantitative models that would help us understand and predict features of the complex cellular systems that underlie human health and disease - think about virtual cells, for example.

Due to popular demand, I’m sharing the PDF access to our paper here: rdcu.be/edo8m I’d love to hear your comments on our review!

Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling 1. Tahoe-100M introduces the largest single-cell perturbation atlas to date, profiling over 100 million transcriptomes across 50 cancer cell lines treated with more

📢 🧬 New preprint! Can we predict which cancer patients will benefit, before treatment begins? Wan Xiang Shen Immunotherapy saves lives but many patients don’t respond to treatment, and we still lack reliable tools to predict who will benefit We introduce COMPASS, foundation