S.Zhang Lab

Important editorial @Nature on the new "AI-scientist" papers "AI scientists can and should empower human researchers. They cannot and should not replace them." nature.com/articles/d4158…

AI has already revolutionized how we write code. Soon, it will revolutionize how we study biology. Biology is not just chemistry; it is code: DNA, RNA, proteins, cells, and evolution interacting across scales. AI will help us read it, reason over it, and eventually write it.

Happy to share our most recent work as we exploring the new field (for us) of genome engineering. 😅 biorxiv.org/content/10.648…

Interested in single cell and spatial genomics? Check out the agenda for our 10th Single Cell Genomics Day on Friday 6/12. Speakers: Aviv Regev @anshulkundaje @junyue_cao @xinjin + many more! All talks are free and live-streamed at satijalab.org/scgd

Transitioning to being a PI in the age of AI Computational biology is in a period of upheaval that is both exhilarating and terrifying. Rapidly, we are approaching a moment of “analytic abundance”, where basically idea you can think of (and several you didn’t) magically appear within minutes of you thinking of them. Of course, the central proximal challenge is the evaluation of the sheer volume results—how do we know they are right when we don’t have the time to check over every line of code? I think it’s very telling that when I talk to AI-pilled faculty, they are exhilerated, but many trainees seem more cautious and far more ambivalent. I think that’s because faculty often have been removed from the details for a long time and probably haven’t checked over a line of code in years. They are used to managing (rather than doing) analysis. Over time, they usually develop a sense for whether things seem right or wrong. In this day and age, this is the skill that you, too, must develop. How do faculty do it? I am guessing every faculty member has their own list of internal sanity checks, but here are a few of mine: * Checksums. I look for things that should add up correctly (percentages add to 100, etc.). If it looks even a little bit off, I ask questions. * Never let go. If something doesn’t make sense, I don’t let go until it does make sense. Never relent! * Explain stray datapoints. Always dig into outliers in the data. How did they come to be? Often, they reveal some hidden assumption or something unexpected about the data. * Do not tolerate warnings. If code gives you a warning, resolve it. Do not continue, do not pass go, until you either understand or eliminate the warning. * Track the number of datapoints. Even a single missing row can be a sign of some fencepost bug. And I’m sure many more that I’m forgetting right now. Basically, it’s a transition from a maker to an interrogator. I also feel it worth reiterating that this is a highly unsetting period of time. I have been fortunate (?) to have 16 years of time to make a transition that people are now being asked to make in months. Again, exhilarating and terrifying, all at once!

Over 250 million protein sequences are known, but fewer than 0.1% have confirmed functions. Today, @genophoria, @BoWang87 & team introduce BioReason-Pro, a multimodal reasoning model that predicts protein function and explains its reasoning like an expert would.

Spring is finally here! 🌱 Looking forward to a new season of fresh ideas, focused writing, and productive days ahead in the lab. Let's make it count. 🔬✨

I am totally impressed by analemma.ai/fars the best part of this that AI researcher is honest and hardworking 😂

We mapped gene interactions across different environmental conditions (GxGxE) at scale for the first time in human cells. These maps lead to the realization that many genes function in a context dependent manner which provides insight into how humans have relatively few genes but many cell types. Congratulations Ben! Paper: cell.com/molecular-cell…

An imaging-free spatial transcriptomics method facilitates high-throughput barcode array generation up to centimeter scale go.nature.com/3QYfSDd rdcu.be/e4Db0

Revealing a coherent cell-state landscape across single-cell datasets with CONCORD go.nature.com/4jsNxSL

Researchers are targeting dormant tumour cells that might explain why some cancers reappear long after successful treatment go.nature.com/4qFvMlG

Pooled genetic CRISPR screening is improved in complex models through leveraging internal controls go.nature.com/4giANvD rdcu.be/eRy4P

Gene context drift identifies drug targets to mitigate cancer treatment resistance dlvr.it/TMyBWv

The creative process in science has its thinking tools, which we all learn eventually. Here are the 12 articles that Martin and I wrote about them, including "It takes two to think" and "A hypothesis is a liability". night-science.org/home/learn/rea… @nightsciencepod @MartinJLercher

Excited to share our review on metabolic adaptations in brain metastasis in @NatureRevCancer!! 👉rdcu.be/exHzY A pleasure to co-write with Pravat @CMTLab_IISERBPR and appreciate the support Daniela @DaniS2911 @utswcancer @UTSWCancerBio @UTSW_Pathology @UTSWMedCenter

A generative artificial-intelligence tool has designed a synthetic CRISPR system that successfully edits human DNA go.nature.com/3JadkRy

📢 Introducing Biomni - the first general-purpose biomedical AI agent. Biomni is built on the first unified environment for biomedical agent with 150 tools, 59 databases, and 106 software packages and a generalist agent design with retrieval, planning, and code as action. This enables Biomni to perform a wide range of research tasks - from literature review, hypothesis generation, protocol design, data analysis, clinical reasoning, and much more - across subfields like genomics, microbiome, physiology, and beyond. Some key results: 🔬 Designed a molecular cloning experiment validated in wet lab, matching the performance of a >5-year expert in a blinded test 📊 Completed a wearable bioinformatics analysis across 458 messy files in 35 min vs. 3 weeks by a human 🧠 Uncovered novel transcription factor hypotheses driving skeletal lineage regulation We built a web platform where biomedical scientists can immediately delegate their tasks to the agent today, completely free! 🧪 Try it now: biomni.stanford.edu 📄 Paper: biomni.stanford.edu/paper.pdf 💻 Code: github.com/snap-stanford/… (will be fully open-sourced very soon!) 💬Join the community: tinyurl.com/biomni-slack Biomni is an open-source initiative: we invite the community to build on it and advance biomedical research at scale. With amazing collaborators @StanfordAILab @StanfordMed @StanfordCancer @genentech @arcinstitute @UCSF @UW @PrincetonAInews @serena2z @hcwww_ @YuanhaoQ @mintaylu @yusufroohani @RyanLi0802 @LinQiu0128 Gavin Junze Di Shruti Jennefer Xin Zhou @MWheelerMD Jon Bernstein @MengdiWang10 @PengHeAtlas @SnyderShot @lecong Aviv Regev @jure

Congrats to our recent Pathology @UTSWGradSchool students for receiving their PhDs! 🎓 Dr. Ryan Kowash – Cancer Bio (Mentor: Esra Akbay) Dr. Samantha Golomb – Cancer Bio (Mentor: Siyuan Zhang) Dr. David Sanchez – Genetics, Development & Disease (Mentor: Diego Castrillon)

Why I teach my students about scientific failure | Science | AAAS science.org/content/articl…