Kexin Huang

give it a try and you may get addicted : )

With amazing collaborators
Serena Zhang Hanchen Wang Yuanhao Qu Minta Lu, PhD Yusuf Roohani Ryan Li Lin Qiu Gavin L. Junze Di Shruti Jennefer Xin Zhou Matthew Wheeler Jon Bernstein Mengdi Wang Peng He Michael Snyder Le Cong Aviv Regev Jure Leskovec

Biomni - A General-Purpose Biomedical AI Agent A general-purpose biomedical AI agent to automate biomedical research.

🌍Biomni is an open-source initiative: we invite the community to build on it and advance biomedical research at scale.

🧪 Try it now: biomni.stanford.edu
📄 Paper: biomni.stanford.edu/paper.pdf
💻 Code: github.com/snap-stanfor... (will be fully open-sourced very soon!)

🔧We built a web platform where biomedical scientists can immediately delegate their tasks to the agent today, completely free!

Powered by:
🧪 Biomni-E1 – the first unified environment for biomedical agent w/ 150 tools, 59 databases, 106 software, systematically curated by mining 2.5K papers in biorxiv
🧠 Biomni-A1 – a generalist agent architecture with retrieval, planning + code as action

🧫 Human-level performance on LAB-bench DbQA and SeqQA, with SOTA across 8 new biomedical tasks—ranging from GWAS and rare disease diagnosis to microbiology and drug repurposing.

Key results:
🔬 Designed a cloning experiment with real-world wet-lab validation; on par with 5+ year expert in a blind test
📊 Ran 458-file wearable bioinformatics analysis in 35 min vs. 3 weeks for human expert
🧠 Uncovered novel TFs regulating skeletal lineages on scRNA+scATAC data

📢 Meet Biomni — the first general-purpose biomedical AI agent.

It automates literature review, hypothesis generation, protocol design, bioinformatics analysis, clinical decision support, and much more — scaling biomedical expertise for 100× discoveries.

biomni.stanford.edu

🧵

Fantastic work by @kexinhuang.bsky.social at Stanford (arXiv: 2502.09858)

Validation of hypotheses for experimental planning and verification based on the falsification principle by Karl Popper.

The Future just most a lot closer!!!

#AAAI Spring Symposium on #AgenticAI for Science is calling for papers. We will have a fun 2.5-day event with speakers and participants from #agents, #LLM and #science communities in #SanFrancisco from Mar 31 to Apr 2, 2025.

A huge shout out to the amazing team!
Tony Zeng, Soner Koc, Alexandra Pettet, Jingtian Zhou, Mika Jain, Dongbo Sun, Camilo Ruiz, Hongyu Ren, Laurence Howe, Tom Richardson, Adrian Cortes, Katie Aiello, Kim Branson, Andreas Pfenning, Jesse Engreitz, Martin Zhang, Jure Leskovec

Small-cohort GWAS discovery with AI over massive functional genomics knowledge graph Genome-wide association studies (GWASs) have identified tens of thousands of disease associated variants and provided critical insights into developing effective treatments. However, limited sample si...

15/15 Paper: medrxiv.org/content/10.1...
Everything is open-sourced at github.com/snap-stanfor...
Talk at Stanford Graph Learning workshop: youtu.be/0_jdg7FqSE4?...

Also, happy to share KGWAS-preview has also won Best poster award at Stanford Bio-X and Reviewer's choice award at ASHG!

14/15 We built a human-AI interaction UI, available at kgwas.stanford.edu where geneticists can explore KGWAS novel variants, genes, cells, and networks across a wide range of diseases!

13/15 KGWAS with MAGMA prioritized disease genes with a 28.3% higher replication rate than GWAS-genes, aligning well with drug targets across 15 diseases. Combined with scDRS, KGWAS identified 44.3% more disease-critical cells across 120 cell types, including a novel B-cell cluster linked to asthma.

12/15 Using a graph XAI method, KGWAS learns disease-specific networks where retained edges explain GWAS signals. These networks align with simulations, expert annotations, and Perturb-seq data. We further applied this approach to Alzheimer’s disease to interpret variant mechanisms!

11/15 GWAS is fundamental and many works have built upon GWAS sumstats - could improvement in KGWAS sumstats translate to downstream GWAS tasks? We illustrate it by using KGWAS to do variant interpretation, disease gene prioritization, and disease -critical cell population detection!

10/15 KGWAS revealed novel associations with strong functional evidence! For example, rs2155219 (11q13) was linked to ulcerative colitis via LRRC32 regulation in CD4+ regulatory T cells, and rs7312765 (12q12) to myasthenia gravis via PPHLN1 regulation in neuron-related cells.

9/15 We then applied KGWAS to 544 uncommon diseases in the UK Biobank (<5K cases), and found 184 more (46.9% more) associations! If we zoom into 144 rare diseases, the gain goes to 79.8% and KGWAS also made >=1 hits for 92 diseases with zero hits in GWAS!

8/15 To study small-cohort GWAS, we downsampled 21 UK Biobank traits (N=374K) to 1–10K samples, applied GWAS methods, and measured replication in the full cohort. KGWAS doubled GWAS power at 1K samples and proved more data-efficient, requiring up to 2.6X fewer samples to match variant associations!

7/15 Extensive null and causal simulations under various configurations (heritability, num. of causal variants) establish that KGWAS is well-calibrated in false discovery control and also has significantly improved power!

6/15 For each disease, we train a GNN to propagate GWAS signals across the KG, using predicted variant associations as priors that capture local GWAS signal concentration, guided by an LD-aware loss. Adjusted p-values are then computed using Genovese et al.’s covariate-based weighting framework.

5/15 We aim to build an AI model integrating multi-modal functional genomics data with GWAS. Using a knowledge graph, we mapped variants-to-genes-to-programs with over 10M nodes, 55 relation types, and rich annotations like PoPS, baselineLD, and embeddings from models like Enformer and ESM.

4/15 How can we uncover novel associations for small-cohort diseases? Emerging functional genomics data (e.g., QTLs, ABC, Hi-C, PPI, Perturb-seq) provide an increasingly clear picture of the cellular functions of a variant. Leveraging this data could intuitively enhance GWAS power!

3/15 However, GWAS requires huge sample sizes, while many diseases of interest, especially rare and uncommon ones, have small cohorts (~hundreds to a few thousand). They often have few significant associations. Yet, these are often diseases with the greatest therapeutic unmet need!

2/15 GWAS performs statistical tests to identify significant genetic variants that are associated with disease (e.g., P<5e-8) by scanning across individuals in large populations. GWAS is crucial—genetics-backed drugs are 2.6X more likely to succeed in trials!

🧬 Thrilled to share Knowledge Graph GWAS (KGWAS), the largest AI model that integrates >10 millions of multi-modal and multi-scale functional genomics data to improve GWAS power by 100% while discovering novel disease-critical variants, genes, cells, and networks!

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