New pre-print "Mendelian randomization in a multi-ancestry world: reflections and practical advice" led by @amymariemason -

📢It’s now out in ⁦@NatureGenet⁩! 🎉Congratulations to our amazing team ⁦@Yibo_Dai⁩ ⁦@GreenLymphoLab⁩ and wonderful collaborators ⁦⁦@MDAndersonNews⁩ #SpatialOmics #TME #inflammatory niches 🌟Data & Code are publicly available

If you don’t exercise, just do 3x1 minute bursts of intense movement daily (rush up the stairs, walk briskly, bike hard, etc) In a UK Biobank study, this was associated with a 40% reduction in mortality

New paper from LABS freshly out at @NatureBME: Brain–heart–eye axis revealed by multi-organ imaging genetics and proteomics. Great team effort from @chrisdav66 at Penn, Keenan Walker, and Michael Duggan, PhD, from NIA! https://t.co/QIRVKSHTOG

Great study comparing genetics of disease susceptibilty vs. disease progression 👉at same sample size more loci identifiable in GWAS of susceptibility vs. progression 👉 genetic architecture of disease susceptibility largely different from that of disease-specific mortality

Today in @ScienceMagazine, we report a new DNA editing technology to seamlessly write massive changes into the right place in the human genome. The reason gene editing hasn't transformed human health is that current gene editing technologies like CRISPR are very limited. The

🚀 Excited to share scPortrait! Led by Sophia Mädler & Niklas Schmacke w/ the Mann lab — a new @scverse tool for standardized single-cell image data. Enables ML-ready extraction, >1B cell processing, cross-omics, & cancer macrophage insights. 🔗 https://t.co/VXHrCBgdDh

Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases https://t.co/Hx86TBZrwG

The human proteome and organ age clocks across the lifespan, a tour de force report @CellCellPress https://t.co/0mlEr8YmOy

Slides for my lecture “LLM Reasoning” at Stanford CS 25: https://t.co/WDI6w0HN8A Key points: 1. Reasoning in LLMs simply means generating a sequence of intermediate tokens before producing the final answer. Whether this resembles human reasoning is irrelevant. The crucial

I am humbled to be recognised alongside my distinguished colleagues. The recognition belongs also to my collaborators in the UK and China, and @unibirmingham, an institution that inspires and empowers its people to make a global difference. https://t.co/jM1HZbIIpi

Announcing Biomni — the first general-purpose biomedical AI agent. Biomni is a free web platform where biomedical scientists can immediately delegate their tasks to Biomni, starting today! Biomni automates literature reviews, hypothesis generation, protocol design,

gsMap paper online. Source code: https://t.co/J1YnfnJ1j6. Results visualisation website: https://t.co/mVKmv21wbZ.

An in-depth analysis of organ-specific proteomic aging clocks in the Whitehall cohort (n=6,235, follow-up 20 years)⏱️ 👉modest correlations (max r=0.37) across organ age gaps 👉larger organ age gaps linked to higher risk of 30/45 age-related diseases 👉only a few aging clocks

Lifestyle is a stronger predictor of aging and mortality than genetics. A new Nature study of ~500,000 people found: 🎯 17% of mortality variation was linked to lifestyle (exposome). 🧬 <2% of was explained by genetics. Here’s what you need to know 👇🧵 1/10

We are introducing a major upgrade to our OmicsPred platform ( https://t.co/yzCepjWZpG)—a resource to enhance the accessibility and usability of genetic scores for multi-omic traits and their phenotypic associations. For more details, please follow us on https://t.co/MCYLpnfKv3

Interesting study❗️ 🔗link to preprint: https://t.co/Y1kXubrsXI

Thrilled that ‘Foundation Models for Biology’ has been named one of the top 7 technologies to watch by @Nature! Thanks for highlighting our scGPT work! Also honored to contribute to such a visionary piece. 2025 will be an exciting year for AI & biology!

Our work of MetaboQTLs in Han Chinese: Genetic mapping of serum metabolome to chronic diseases among Han Chinese: Cell Genomics

Cross-omics links enabled by deep phenotyping in UKB❗️ 👉2,923 proteins & 1,051 organ imaging traits 🧠🫁🫀 👉5,067 protein-imaging associations 👉proteins associated with most organ imaging traits tended to be expressed in these organs 👉in MR 8,116 protein-imaging trait links