They were learned during training along with other parameters. See page 31 of the manuscript detailing where they were incorporated.

The first draft of the human genome turns 25 today! 🎂

On 26 June 2000, the UK & US announced the first full map of the 3 billion DNA base pairs that make us human.

🌍 A milestone for humanity, powered by global collaboration.
www.youtube.com/watch?v=pnWA...

#HumanGenome #ScienceHistory 🖥️🧬

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 alphagenomecommunity.com 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 (older generation) 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 goo.gle/3ZPUeFX along with the preprint goo.gle/45AkUyc describing and evaluating our latest DNA sequence model powering the API. Looking forward to seeing how scientists use it! @googledeepmind

Fantastic start of the #VIBAIsymposium this morning with a welcome by our scientific director @steinaerts.bsky.social and talks by Julien Gagneur and @avsecz.bsky.social

Hello world! Refreshing to see my feed full of science again:)