Jingyou Rao

Incoming Postdoc @UCSF | PhD in Computer Science @UCLA protein mutational scanning analysis

[Boyang Fu @Boyang1995]

For predicting genetic perturbation response, some researchers scale vertically with more perturbation data; we chose to scale horizontally -- connecting to DNA.

A memorable milestone for my first postdoc project at @marinkazitnik's ZitnikLab!

[Yun S. Song @yun_s_song]

Our article "Learning antibody sequence constraints from allelic inclusion" is now published in Cell Systems and freely available from this link until Oct 2, 2025:
https://t.co/qCZERPUMPF

Previous post on the article:
https://t.co/ZEiKBWiWKi

[Judea Pearl @yudapearl]

Thanks for posting. I wasn't aware of this beautiful exposition by @rlmcelreath, which I highly recommend for curious statisticians. Primer is now available free of charge:
https://t.co/qvlV7qJaAr
Including Homework and Solution Manual, which we have released for enlightened instructors of statistics. @f2harrell

[Jingyou Rao @JingyouR]

Try Cosmos in Python now!
See vignettes to get started
🔗 Code: https://t.co/MNw9J6fA2i

[Jingyou Rao @JingyouR]

How do we decouple the effects of two functional phenotypes in protein deep mutational scanning (DMS)?
Meet Cosmos, our new statistical framework for causal inference in multi-phenotype DMS.
https://t.co/ZOy32xLJ5T
[1/n]

[Jingyou Rao @JingyouR]

We apply Cosmos to 3 DMS datasets:
Kir2.1: abundance → surface expression
PDZ3: abundance → CRIPT binding
KRAS: abundance → RAF1_RBD binding
Cosmos clearly separates direct binding residues from those with indirect effects.

[Le Cong@Stanford, AI+Bio+Gene-Editing @lecong]

🧬 BREAKING: Our CRISPR-GPT paper is out TODAY in Nature Biomedical Engineering @natBME !
🤯 We built an AI agent that turns ANYONE into a gene-editing expert in 1 DAY instead of months. An undergrad with ZERO experience achieved 90%+ editing efficiency on their FIRST attempt.
🧵 Here's how we're building expert AI agents for cutting-edge biotechnology:
🎯 The Problem: CRISPR is revolutionary but requires PhD-level expertise, it can take weeks to learn, adopt, and design, analyze a CRISPR experiment for R&D or making life-saving medicine. Even Pro scientists can make small mistakes (e.g. typos in guideRNA or cloning design) that cost months to find out, slowing us down.
💡 Our Solution: CRISPR-GPT - an AI co-pilot from @Stanford @Princeton @GoogleDeepMind that guides you through EVERY step via simple conversation
🔬 Real Results:
-Novice researcher: ~90% editing on 1st go
-Training time: Months → 1 day
-100% success rate in our trials
-Even experts save days/weeks on data analysis & troubleshooting
🤖 How it works: Our multi-agent system handles: CRISPR system and delivery method selection, guideRNA design, Protocol generation, Real-time troubleshooting, Data analysis, and beyond. All through natural language! No coding, no complex software.
📊 We benchmarked it extensively:
-288 evaluation scenarios/cases
-Outperformed GPT-4o on ALL gene editing tasks
-Trained on 11 years of expert discussions
-Covers knockout, base-editing, prime-editing & epigenetic editing
🌍 Why this matters:
-Every lab can now use CRISPR with an AI system distilling expert knowledge and skills.
-Every student can learn faster.
-Every researcher can tackle bigger challenges without worrying about small mistakes.
-Customized CRISPR design can be automated based on your need and the context of R&D workflow.
-Agentic AI ensure safety, privacy, and responsibility
-We're not just automating gene editing - we're using AI to power scientists to cure diseases.

🚀 Try it yourself! Beta access available at: https://t.co/CFQzrdVVPW
Paper: https://t.co/WOgmAjg1Ed
Code: https://t.co/UnPKrxyJuW
Benchmark (companion work, Genome-bench): https://t.co/LIooAXj2op

Co-first and key authors: @YuanhaoQ @KaixuanHuang1 @MingYin_0312
PIs: @lecong @MengdiWang10
Key collaborators: @Rbaltman @denny_zhou
The future of biology and science is conversational. The future is now. @natBME @NaturePortfolio

#CRISPR #AI #GeneEditing #Biotech #Science #AISafety

[Uthsav Chitra @uthsavc]

Presenting at #ISMBECCB2025 tomorrow!
GASTON-Mix, a unified model of spatial gradients and domains in spatial 'omics data.
11:20am UK time at RegSys

Also recruiting students for my new lab at @JHUCompSci, feel free to reach out if you want to chat

[Yuzhe Yang @yang_yuzhe]

Check out our latest foundation model for wearable sensor data (LSM-2)!

[Le Cong@Stanford, AI+Bio+Gene-Editing @lecong]

🚀 Join CongLab @Stanford! We’re hiring postdocs to create lab-in-the-loop self-evolving AI agents, open benchmarks, to design, test & learn—advancing safer gene & cell therapies. Build on CRISPR-GPT, RNAGenesis model, Genome-Bench, for innovative medicines. #PostdocJobs

[Jingyou Rao @JingyouR]

@elezzx2020 Thank you Zixuan!

[Jingyou Rao @JingyouR]

Thrilled to share that I just successfully defended my PhD! Thanks to my committee, collaborators, and everyone who’d supported me throughout my seven years at UCLA. A special thank you to my PI Harold for his incredible mentorship! @hjpimentel

[Zeyun Lu 鲁泽沄 @zeyun_lu]

Happy to share that our work from the @nmancuso_ lab is out in @NatureGenet! We developed SuShiE, a multiancestry fine-mapping method for molecular traits. https://t.co/8gySuJkDEw

[Jingyi Jessica Li (李婧翌) @jsb_ucla]

I’m honored to join Fred Hutch as Professor and Program Head of Biostatistics, and as the Donald and Janet K. Guthrie Endowed Chair in Statistics. Excited to be part of a deeply collaborative and scientifically vibrant community with a rich legacy of impact.

[Arc Institute @arcinstitute]

Introducing Arc Institute’s first virtual cell model: STATE

[Kalli Kappel @KalliKappel]

In the nucleus, many intrinsically disordered proteins (IDPs) form condensates. What IDP sequence features drive this behavior? We developed CondenSeq, a high-throughput method to measure nuclear condensate formation & applied it to ~14000 IDPs to find out
https://t.co/qfa8jtvFOp