𝓟𝓮𝓷𝓰 𝓗𝓮 何鵬

Our Perspective paper "Toward informed batch correction for single-cell transcriptome integration" is out in @NatComputSci 🔗rdcu.be/e4cSp We review a decade of batch-correction methods and propose to move from "blind" integration to "informed" modeling.🧵👇

Zhang et al demonstrate that removal of #Alk1 in #AECs of the mouse establishes a preclinical model of hereditary hemorrhagic telangiectasia (#HHT). Learn more about the mechanism of severe #nosebleeds at ahajrnls.org/4vOh6nH @mf_navedo @_kyleJacobs @PengHeAtlas

I've restarted curating this job list and have added 24 PI jobs this month. Please help share with those on the market 😊

200 jobs curated now

I'm maintaining an active list of biology-related job openings at various levels (PIs, staff scientists, postdocs, PhDs, etc.). Please feel free to subscribe or contribute! github.com/brianpenghe/Bi…

📢New Perspective out! @PengHeAtlas, Sarah Teichmann and colleagues review widely adopted batch correction methods and propose a path toward more informed, context-aware approaches for future method development. bit.ly/4bYAvef 🔓rdcu.be/e4pUJ

This paper wouldn't be possible without my wonderful co-authors: Shuang, Malte, @MarioniLab and @teichlab

8️⃣The Future: leverage high-quality references and interpretable models to convert "unknown" Class II effects into "known" Class I artifacts. 🔄 Long-term🚀: Robust reference-based analyses resilient to batch effects—ultimately reducing the need for post hoc correction altogether

Our Perspective paper "Toward informed batch correction for single-cell transcriptome integration" is out in @NatComputSci 🔗rdcu.be/e4cSp We review a decade of batch-correction methods and propose to move from "blind" integration to "informed" modeling.🧵👇

Just published a comprehensive protocol in @STARProtocols @CellPressNews on how to achieve high efficiency CRISPR-based genome editing in primary HSPCs, which we hope will be a resource for the field! star-protocols.cell.com/protocols/4394

🧬 Excited to open-source Biomni! With just a few lines of code, you can now automate biomedical research with AI agent! We are releasing Biomni A1 (agent) + E1 (env) with 150 specialized tools, 59 databases, and 105 software. E1 is our first attempt at curating the bio-agent environment, but it only scratches the surface: Call to help build Biomni-E2 - an open environment for bio-agent, built with and for the community! We welcome new tools, benchmarks, datasets, agents, and beyond to build the environment together. Significant contributors will be invited as co-authors on our upcoming E2 manuscript. More to come! 💻 github.com/snap-stanford/… 🌐 biomni.stanford.edu @ProjectBiomni

Great paper proposing a graph/neighborhood-based way to supplement current metrics in single-cell integration benchmarking. Excited to see our fetal lung cell atlas used to guide metric evaluation. Biology is continuous—and so should be our thinking, including in AI. 👏🧬🤖

📢 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

@hhhcce @IcahnMountSinai @MountSinaiPsych @SinaiBrain Congratulations Cheen Euong! 💐

I am very excited to announce that the Ang lab is opening at @IcahnMountSinai @MountSinaiPsych @SinaiBrain. We are hiring at all levels to join us in developing spatial omics and stem cell tools. If you're interested, please email me at cheeneuong.ang at mssm dot edu

1/7 Planning to build a single-cell atlas? Or wondering how atlases can be useful to your research? Read our guide on single-cell atlases nature.com/articles/s4159… published today in Nature Methods, by @SikkemaLisa, @KHrovatin, @MDLuecken, @fabian_theis and others.

Great opportunity!