Our latest, from the brilliant Will Chen and Junhong Choi, out in nature , is ENGRAM, a multiplex method for symbolic recording of signaling and cis regulatory activity to DNA. Final version (not in preprint) includes integration w/ DNA Typewriter. rdcu.be/dN62L

Hosting a biotech mixer in Boston with Pillar VC! Looking forward to bringing the community together — Thurs, Aug 8 @ 6:00PM lu.ma/xqqn0kw3

cool boston co-working experiment!! Satvik Dasariraju & I hosted a co-work night last week w/ Harvard/MIT founders & PhDs We hope to do this on a weekly basis. Apply to join! Might even have an impromptu crew co-work this weekend :) forms.gle/czwXu8sPyKdGqg…

My favourite discovery ever has just come online. Can I please tell you about some seriously wacky molecular biology? The story starts with a reverse transcriptase that SOMEHOW defends bacteria from viruses. (👇 I recommend sound ON for the video 🎹) 1/

YC S24's Focus Buddy is building an AI co-worker for 140 million people worldwide who struggle with ADHD. Their users speak to the AI 6+ hours every week to get more work done. Congrats on the launch, Adnan Sherif and Yash Ramchandani! ycombinator.com/launches/Lns-f…

Smell is the least-understood of our senses. With Judith Amores and Seyone Chithrananda, we trained biologically-inspired models that first map odorant molecules to their receptor activation profiles and subsequently predicts their odor percepts.

Really excited to finally share this work from my internship at Microsoft Research in summer 2023! This project began when Judith and Kevin raised a simple question: can we develop better latent mappings of olfactory stimuli, such as molecules to odor perception? Summary (1/12):

Excited to announce our new work! 🧬 Some highlights are: - sequences likelihoods predict zero-shot fitness capabilities - a new method to calculate pLM likelihood in O(1) instead of O(L) forward passes - providing a causal between training data and outputs - suggesting a new

Launching FocusBuddy - your AI productivity Co-pilot Check us out: producthunt.com/posts/focus-bu… An AI that stays on call in the background while you work: • Uses a voice-first to-do list that **updates itself as you work** • Checks in with you to hold you accountable • Finds

Modern GWAS can identify 1000s of hits but it can be hard to turn this into biological insight. What key cellular functions link genetic variation to disease? I'm excited to present our new work combining associations + Perturb-seq to build interpretable causal graphs! A🧵

It’s been a tough few weeks. My 10yo daughter was diagnosed with a very rare, aggressive cancer called interdigitating dendritic cell sarcoma (IDCS). I’m reaching out to identify clinicians/patients who have encountered pediatric IDCS, indeterminate dendritic cell histiocytosis

Announcing Encode: AI for Science We're launching a fellowship powered by ARIA to connect top AI talent with leading UK science labs to unlock the next wave of scientific breakthroughs.

A new IGI article delves into the story behind our method for statistically guaranteed genome mining and discovery of genes of unknown function. The piece offers insights into the journey and motivation driving our work! Read more here: innovativegenomics.org/news/rapid-ai-…

the IGI wrote a bit about our (in progress) work on building statistical tools for genome mining and discovery! check it out below ⬇️ 🔍

Nucleate Dojo Reunion!

🚀 Fellowship applications are OPEN for Encode: AI for Science. What if you could use AI to - Design shape-shifting robots - See through solid materials - Decode language of the brain - Create advanced materials

Patrick Collison The first that comes to mind is biology and ML. Seyone Chithrananda and Ayush Noori are good examples. Both have publication records that exceed many grad students, all before finishing undergrad or even high school. Both are in top labs now, but my guess as to why Bio+ML is accelerating

Our paper has been published Nature Genetics! Through new statistical methods, we shed light on fundamental questions about cellular response to genetic perturbations. Our work is a substantial advance towards rigorous characterization and comparison of massive perturbation atlases.

In advancing AI in Bio, much gains come from better models and generating large datasets, but the biggest gain may be in inventing net new methods for data generation for new measurement modalities and at larger scale

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With