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𝓟𝓮𝓷𝓰 𝓗𝓮 何鵬

@PengHeAtlas

bioinformatics for multiome➕spatial➕single-cell; development↔disease; Assistant professor at UCSF; Tweets👉own;
BlueSky: PengHe.bsky.social
San Francisco, CA peng-he-lab.github.io Joined January 2013
1.7K Following 1.3K Followers 1.1K Posts

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𝓟𝓮𝓷𝓰 𝓗𝓮 何鵬 @PengHeAtlas
1/8 What remains undiscovered in the human kidney? 🔍

While building an integrated human kidney atlas for the Human Cell Atlas, we discovered new biology—presented in our new preprint, with highlights below. 🧵

biorxiv.org/content/10.648…
www.biorxiv.org An Integrated Atlas of the Human Kidney Spanning Health and Disease The human kidney contains highly specialized cell populations. Despite numerous single-cell and single-nucleus transcriptomics studies, differences in cohorts, technologies, analytical pipelines, and...
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𝓟𝓮𝓷𝓰 𝓗𝓮 何鵬 @PengHeAtlas
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. 👏🧬🤖
Hanchen Wang Verified account @hcwww_ Jun 11, 2025
Defining “what is good” is central to both AI and Science.

Despite massive efforts towards building the Virtual Cell, our recent @NatureBiotech article reflects on whether current gold standards (i.e., evaluation metrics) still hold up.

nature.com/articles/s4158…
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𝓟𝓮𝓷𝓰 𝓗𝓮 何鵬 @PengHeAtlas
Great opportunity!
evo-devo Verified account @Xiaojie_Qiu Dec 16, 2024
We are hiring! Join us in building virtual embryo models — an ambitious and exciting mission that goes beyond the widely acclaimed “virtual cell” initiative! 🚨

Are you a technology developer, developmental biologist, or machine learning expert interested in creating predictive, virtual 3D mammalian embryos to advance human health, particularly for congenital heart diseases?

We’re looking for applicants with expertise in genomics, developmental biology, generative AI, or related fields to join our visionary project.

The selected candidate will work within a multidisciplinary team of biologists, engineers, machine learning experts, mathematicians, and physicists.

We offer an enriching, collaborative environment where post-docs, students, and team members can develop into leaders in both academia and industry.

Join us on this exciting journey to shape the future of human health! ! 🚀

Nature career link below: nature.com/naturecareers/…

Email: xiaojie at stanford dot edu
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