# We don't know what most microbial genes do. Can genomic language models help? (Yunha Hwang, Ep #7)

Page: https://stenobird.com/podcast/owl-posting-6974455/we-don-t-know-what-most-microbial-genes-do-can-genomic-language-models-help-yunha-hwang-ep-7
Text version: https://stenobird.com/podcast/owl-posting-6974455/we-don-t-know-what-most-microbial-genes-do-can-genomic-language-models-help-yunha-hwang-ep-7.md
Podcast: [Owl Posting](https://stenobird.com/podcast/owl-posting-6974455)
Published: 2025-12-08T22:04:11+00:00
Episode link: https://www.owlposting.com/p/we-dont-know-what-most-microbial
Audio file: https://api.substack.com/feed/podcast/181001283/30a10c7418087cd987afdbf493685d82.mp3
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Duration seconds: 6162

## Resource

Note: Thank you to rush.cloud and latch.bio for sponsoring this episode! Rush is augmenting drug discovery for all scientists with machine-driven superintelligence. LatchBio is building agentic scientific tooling that can analyze a wide range of scientific data, with an early focus on spatial biology. Clip on them in the episode. If you’re at all interested in sponsoring future episodes, reach out! *** This is an interview with Yunha Hwang , an assistant professor at MIT (and co-founder of the non-profit Tatta Bio ). She is working on building and applying genomic language models to help annotate the function of the (mostly unknown) universe of microbial genomes. There are two reasons you should watch this episode. One, Yunha is working on an absurdly difficult and interesting problem: microbial genome function annotation. Even for E. coli, one of the most studied organisms on Earth, we don’t know what half to two-thirds of its genes actually do. For a random microbe from soil, that number jumps to 80-90%. Her lab is one of the leading groups working to apply deep learning to solving the problem, and last year, released a paper that increasingly feels foundational within it (with prior Owl Posting podcast guest Sergey Ovchinnikov an author on it!). We talk about that paper, its implications, and where the future of machine learning in metagenomics may go. And two, I was especially excited to film this so I could help bring some light to a platform that she and her team at Tatta Bio has developed: SeqHub . There’s been a lot of discussion online about AI co-scientists in the biology space, but I have increasingly felt a vague suspicion that people are trying to be too broad with them. It feels like the value of these tools are not with general scientific reasoning, but…

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