Very excited to share our work at @GoogleDeepMind on missense variant pathogenicity prediction using AlphaMissense, new in @sciencemagazine https://t.co/XL8GPeiyh3. Long 🧵focused on more technical details. 1/

My Journal Club on Enformer, an AI model that predicts gene expression from DNA, is out in @NatureRevGenet This is a high-level look at how Enformer advanced seq2fun AI, and the challenges that remain in modeling gene regulation. (Easy 5 min read) 🔗

Thrilled to introduce AlphaGenome, our new DNA sequence model now available via our AlphaGenome API. Really excited to see how the scientific community uses AlphaGenome’s predictions to understand genome function, drive biological discoveries, develop new treatments, and more...

Excited to share #AlphaGenome, a start of our AlphaGenome named journey to decipher the regulatory genome! The model matches or exceeds top-performing external models on 24 out of 26 variant evaluations, across a wide range of biological modalities.1/6

Happy to introduce AlphaGenome, @GoogleDeepMind's new AI model for genomics. AlphaGenome offers a comprehensive view of the human non-coding genome by predicting the impact of DNA variations. It will deepen our understanding of disease biology and open new avenues of research.

This a really exciting leap forward for genomic sequence to activity gene regulation models. It is a genuine improvement over pretty much all SOTA models spanning a wide range of regulatory, transcriptional and post-transcriptional processes. 1/

Thanks to the large consortia like ENCODE, FANTOM5, GTEx, 4DNucleome for generating key training data. And finally, thanks to numerous researchers developing inspiring DNA sequence-to-function models, informative evaluations, and interpretation tools.

Big thanks to the amazing team! They went above and beyond to get us here. It's a big privilege to work with them. Also thanks to our early testers for their feedback.

This model represents a start, and not the end, of our AlphaGenome named research and release journey aiming to help researchers better understand the genome using AI. We’d love to learn about how you’re using it. You can share projects & feedback in https://t.co/IO4Zg6xAhX forum

A single model can be more convenient to use and deploy - saying this as someone who worked on a model zoo before. SOTA performance across diverse tasks suggests that fine-tuning it on new data should work quite well. One could also extend it with new assays in the future.

This all-in-one model was achieved by lots of amazing engineering that unlocked training on base-resolution data with long sequence context without increasing the total training compute -training a single model actually used half of what we used for Enformer on the same hardware.

I see this model as a fusion of many different DNA sequence-to-function models tackling different steps of gene expression. It also has important new features like splice junction prediction to better capture the complexity of different splicing events.

Excited to launch our AlphaGenome API https://t.co/gL6Je8t2xf along with the preprint https://t.co/ZeLzhR7VBl describing and evaluating our latest DNA sequence model powering the API. Looking forward to seeing how scientists use it! @GoogleDeepMind

I’m excited to co-host the new #AIxBio conference alongside @Avsecz, @ferruz_noelia, @pdhsu, @BenLehner, @ml19, @deboramarks, @p_tingying, @CarolineUhlerat at @wellcomegenome/ @sangerinstitute! We’ll focus on modellingz and designing DNA, RNA, proteins, and cells, as well as

Excited to share our latest preprint, as part of ENCODE4, on a consensus variant-to-function (cV2F) score for functionally prioritizing variants for complex disease - led by Tabassum Fabiha and co-mentored with Alkes Price. https://t.co/yCIjgODnY9

If AlphaFold was proof of AI’s potential, it is now also prelude – to a future of incredible progress, driven by AI, shaped by human ingenuity, and guided by a collective sense of responsibility and safety. Massive congrats to @demishassabis and John on winning the Nobel Prize!

Huge congratulations to @DemisHassabis and John Jumper on being awarded the 2024 Nobel Prize in Chemistry for protein structure prediction with #AlphaFold, along with David Baker for computational protein design. This is a monumental achievement for AI, for computational

Really pleased to see DeepRVAT published. A new method for rare variant genetics by @Holtkamp_Eva & @brianfclarke in collab with @gagneurlab. I like to think of DeepRVAT as one network on top of another, harnessing variant annotations from bags of models- AlphaMissense and alike.

🧵 1/9 Delighted to announce that #DeepRVAT is out today in Nature Genetics! Here's a recap of our tweetorial from the preprint, with updates for some exciting new results. It's been a joy to work with @Holtkamp_Eva, as well as @gagneurlab and @OliverStegle.

Solving International Maths Olympiad (IMO) level problems has been a grand challenge for AI systems. Happy to share that a new solver (AlphaProof) developed by our team @GoogleDeepMind and our geometry solver (AlphaGeometry) were able to solve 4 out the 6 IMO 2024 problems!

Advanced mathematical reasoning is a critical capability for modern AI. Today we announce a major milestone in a longstanding grand challenge: our hybrid AI system attained the equivalent of a silver medal at this year’s International Math Olympiad!