Alexander Sasse

@lxsasse.bsky.social

Learning from algorithms that learn from data about gene regulation.

"Hence our dismal conclusion: rather than finding that LLMs free us to do a better job of what we were doing before they came along, they shift scientific incentives (and the playing field of academic competition) in ways that compel us to do more and more, faster and faster, less and less well."

Carl T. Bergstrom@carlbergstrom.com · 2w ago

1. We—@eduede.bsky.social, @mjcrockett.bsky.social, Kevin Gross, and I—have a new preprint on the arXiv today, based on ideas that emerged during an @sfiscience.bsky.social workshop in November 2024: The unintended consequences of large language models as a labor-augmenting technology in science.

A new Science study shows that bumble bees can position a ball underneath a fake “flower” to reach a reward, suggesting they can exhibit spontaneous problem-solving and challenging the notion that such advanced cognitive abilities are exclusive to large-brained vertebrates. https://scim.ag/4vs08dC

New paper! How do RNAs "know" where to go inside a cell? We dug into the sequence elements that route RNAs to the right place. It turns out that, in mammals, they're surprisingly massive (>200 nt), multipartite, and wonderfully complicated. 🧵

Large-scale transcriptomic data across four mammalian species and multiple tissues identify conserved molecular signatures associated with ageing and mortality risk. The integration across species, tissues, perturbations, and interventions is a notable strength.

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Huge congrats from our whole lab to Sir David Attenborough on his 100th birthday!!! 🎉🥳❤️ I had the honor of meeting him - and being interviewed by him - in 2013 for his BBC documentary "Rise of Animals: Triumph of the Vertebrates", which featured part of our work.

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