Excited to share our work introducing EPInformer🧬, a scalable and lightweight deep learning framework to predict gene expression by integrating promoter-enhancer sequences with epigenomic signals and chromatin contacts. 📜 https://t.co/YSWOsueEJZ (1/11)

Exciting new joint study out in @Nature today from Mineto Ota (Marson and @jkpritch labs) - Causal modelling of gene effects from regulators to programs to traits

The model of gene expression taught in school is highly misleading! Transcription factors are proteins that bind to DNA and then help repress, or activate, the expression of genes. Cells have hundreds of different types of transcription factors, each tuned to regulate different

Excited to share our new paper on predicting gene expression in yeast! We introduce "Shorkie," a supervised ML model that builds off a self-supervised foundation to interpret regulatory DNA. Preprint:

Excited to share that Ctrl-DNA, our constrained RL + Genomic Language Model system for cell-type–specific regulatory DNA design, co-led with @xingyuchen67, was accepted as NeurIPS 2025 Spotlight (top 3.2%) 🧬✨ Paper: https://t.co/kZHZ4YFcdA Code: https://t.co/wO42Qv2chY

Excited for a major milestone in a collab effort led by @jengreitz to map enhancers & interpret variants in the human genome: The E2G Portal https://t.co/O9CRC1gRsf collates predictions of enhancer-gene regulatory interactions across >1,600 cell types & tissues. Use cases 👇1/

OmniPath ( https://t.co/g2JRIODn2k): integrated knowledgebase for multi-omics analysis https://t.co/SYaCvU76su 🧬🖥️🧪 Python module https://t.co/n7As1pmtmv R package https://t.co/bwzjAmuab8 #Rstats

Latest genomic AI models report near-perfect prediction of pathogenic variants (e.g. AUROC>0.97 for Evo2). We ran extensive independent evals and found these figures are true, but very misleading. A breakdown of our new preprint: 🧵

Ever wish you could hit "undo" on disease? 🩺🔄 https://t.co/VW9BSsvJd7 Most drug discovery asks: what does this perturbation do to cells? But we can also ask the reverse: which perturbations undo a disease signature and move cells back toward health? That's the idea behind

This is the single most important resource for genomics research. Earth’s DNA is now at our fingertips. What a heroic effort and mega congrats to @RayanChikhi and @RNA_Life!!!

In the genomics community, we have focused pretty heavily on achieving state-of-the-art predictive performance. While undoubtedly important, how we *use* these models after training is potentially even more important. tangermeme v1.0.0 is out now. Hope you find it useful!

In April '25, I shared the origin story of Evo on the TED stage. I talked about the motivation behind generating DNA with AI and how it could change what’s possible. It was an incredible experience. full video: https://t.co/tstT6cQI6u

🚀 OncoGAN is now published in @CellGenomics! We introduce an AI system that generates high-fidelity, privacy-preserving synthetic cancer genomes — now open-access. 🔍 Why OncoGAN? --No patient data leakage — critical for genomic privacy --Built-in ground truth — ideal for

Tonight’s reading material. A team at DeepMind wrote this piece on how you must think about GPUs. Essential for AI engineers and researchers.

🚀 Introducing PantheonOS ( https://t.co/NZM3wcHXbG): A Fully Open-Source Agent OS for Science PantheonOS began as a research project in my Stanford lab and has since evolved into a vision to redefine data science in the era of AI—starting with computational biology, especially

I'm glad that I had a chance to contribute to this wide-ranging article discussing the myriad ways ML is being used in genomics:

Introducing STELLA — a Self-Evolving LLM agent that autonomously creates its own tools to navigate and accelerate biomedical research. 🤖 Why It Matters: ⛓️ The Limitation: Most AI agents are fundamentally limited by a fixed set of predefined tools. This is a major bottleneck

🤝Excited to partner with Tamarind @kavi_deniz to build towards an agentic AI protein designer. 🔁 Agentic protein optimization — Starting from a sequence, Biomni iteratively improves thermostability by orchestrating AlphaFold-2, ThermoMPNN, and reasoning over predictions and

reprogramming cells with transcription factors is our most expressive tool for engineering cell state traditionally, we found TFs by ~guesswork @icmlconf we're sharing @newlimit's SOTA AI models that can design reprogramming payloads by building on molecular foundation models

Excited to be at #ISMBECCB2025! I'll be presenting at RegSys on "Learning the Regulatory Genome by Destruction and Creation" (Thursday 3:15PM in Room 11BC). I'm looking forward to connecting with both new and old friends throughout the conference! 🧬 #CRISPR #Genomics #Diffusion

Some aspects of AI discourse seem to come from a different planet, oblivious to basic realities on Earth. AI for science is one such area. In this new essay, @sayashk and I argue that visions of accelerating science through AI should be considered unserious if they don't confront