Ed Cartwright

@pushmeet @GoogleDeepMind This is great! Thank you

I am happy to introduce AI co-clinician, @GoogleDeepMind's research initiative to explore how AI could better amplify doctor's expertise and help deliver higher quality care to patients. We’re excited about our early results, and are taking a phased approach to our research explorations with academic and research collaborators. Read more in our blog: deepmind.google/blog/ai-co-cli…

@cgeorgiaw This is just great, Hugging Science, I just love it. I have many suggestions eg bacterial datasets downloaded paper by paper that have been accumulated on various hard drives. Have already spotted databases I want to use. Thank you!

🤗🤗🤗introducing Hugging Science -- the home of AI for science 🤗🤗🤗 open models and datasets are the powerhouse of science (see the PDB), but finding the models and data you actually need for your breakthrough is hard af you shouldn't need to scrape arxiv, own your own wetlab, fight a custom HDF5 parser, build a fusion stellarator, and beg for compute before you've trained a single epoch so we're changing that we've put all the best science on @huggingface in one place: - 78GB of genomics data - 11TB of PDE simulations - 100M cell profiles - 9T DNA base pairs - 13M molecular trajectories - 400k medical QA pairs and much more, all open, and all ready for training (+ you can also now filter and search by domain, task, and keyword) we've put together all the biggest releases from our partners at NASA, Google, OpenAI, Meta FAIR, Arc Institute, Ginkgo, SandboxAQ, Proxima Fusion, NVIDIA, Ai2, OpenADMET, InstaDeep, Future House, Polymathic AI, LeMaterial, Earth Species Project, Merck, and Eve Bio if you're not sure where you fit in -- work on open challenges for problems that matter: including fusion stellarator design, ADMET, antibody developability, multilingual medicine, catalysis and materials, and scientific reasoning. we're already changing how science gets done: a fusion startup needed a benchmark for stellarator plasma confinement that didn't exist. @proximafusion shipped ConStellaration on Hugging Science: a leaderboard, dataset, and eval metrics, all in one place. a drug discovery team wanted to predict hPXR induction. OpenADMET put up a blind challenge: 11,000+ compounds assayed at Octant, 513 held out, two tracks (pEC50 + structure). Anyone in the world can train and submit. an antibody team at @Ginkgo released GDPa1, a developability dataset for stability, manufacturability, and immunogenicity prediction, with a live leaderboard scoring every submission. if you know a problem the ML community should be working on, let us know. make a challenge! this is about putting all the tools for solving science in one place. so we can hillclimb! → huggingscience.co

DHSC needs to give them the political cover to do so. Otherwise the pattern repeats: useful clinical kit quietly switches off UK access, and frontline clinicians pay the cost of regulatory ambiguity we never actually legislated.

OpenEvidence, one of the most useful tools in my clinical week, gone from the UK. And we don’t even have the EU AI Act. We’ve inherited the chilling effect without the rules. MHRA needs to clarify whether clinical evidence retrieval tools fall inside the device perimeter.

“We are ethically aligned, do not push against your terms of service, ensure all agents make this clear”

I would do the bioinformatics myself at this point but I've already forgotten how

@kenjmloi Really enjoyed this. The structural homology approach via clustered AFDB to find something this divergent is a lovely demo of where discovery biology is heading. And the wobble-position skip rule as anti-escape mechanism is a beautiful piece of evolutionary logic. Congrats to all

Excited to share our discovery of a new programmable RNA-guided DNA-targeting system hiding inside bacteriophages that predates CRISPR. We call it VIPR (Viral Interference Programmable Repeat), and it uses an entirely new logic to find its targets. Thread + link below.

@KyleWSwanson Cool paper Kyle! I like the approach

SyntheMol-RL has now been published! SyntheMol-RL is a reinforcement learning model for synthesizable small molecule drug design. We used it to design antibiotic candidates for the bacteria S. aureus with hits validated in vitro and in vivo in mice. 1/6 link.springer.com/article/10.103…

Our paper in @Nature today 🥳 We tracked 6,438 mice from puberty to death and mapped the genetics of *when* you die, not just whether a gene associates with lifespan. nature.com/articles/s4158… 59 loci. Two decades of data. Thread 👇 #Longevity #Aging #Genetics #Healthspan

BREAKING: Tim Cook steps down. Ternus to CEO.

@patrickc They cite neuroinflammation and caloric diversion from brain development during peak plasticity, which would apply to any pathogen causing illness severe enough to hospitalise an infant. Interesting for vaccine effectiveness (eg new RSV)…

@patrickc Cool study. Makes you wonder what organisms are doing the damage - in under-1s it’s going to be RSV etc (bronchiolitis causes 7X asthma risk), but the earnings, education and mental health effects suggest something beyond airway remodelling…

There is a hypothesis that birth order effects (on things like income and educational attainment) are in part respiratory pathogen effects: younger kids get more of them from their older siblings. This cool recent paper uses Danish administrative data to argue that this is true and a pretty large part of the story. (They claim 70% of the birth order effect on long-run wages.) Other work has previously shown that severe infections matter for long-run outcomes, and it's well-established that birth order matters, but I haven't until now seen anyone convincingly show that standard respiratory pathogens impose long-term costs on infant siblings. nber.org/system/files/w…

My dear front-end developers (and anyone who’s interested in the future of interfaces): I have crawled through depths of hell to bring you, for the foreseeable years, one of the more important foundational pieces of UI engineering (if not in implementation then certainly at least in concept): Fast, accurate and comprehensive userland text measurement algorithm in pure TypeScript, usable for laying out entire web pages without CSS, bypassing DOM measurements and reflow

@_chenglou This is very good!

Someone with an unlimited Codex token budget just committed to rewriting every major genomics library in highly performant Rust. This is going to be amazing if it works.

Today in @NatureMedicine we report that AI can predict 130 diseases from 1 night of sleep🛌 We trained a foundation model (#SleepFM) on 585K hours of sleep recordings from 65K people—brain, heart, muscle & breathing signals combined. AI learns the language of sleep🧵