Ada Fang

@AdaFang_

PhD Candidate @Harvard | AI for Scientific Discovery & Biology | ex @GoogleDeepMind SR

Cambridge, MA

Joined August 2022

264 Following

6,327 Followers

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Ada Fang
@AdaFang_
May 28
AI Scientists are starting to actually do science. Not just answer questions. Not just run workflows. Introducing AutoScientists: a decentralized team of AI agents that can generate hypotheses, design experiments, write code, test ideas, analyze failures, and revise strategy as
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Ada Fang
@AdaFang_
Oct 2
Thanks @anshulkundaje for recognizing our work! I think a lot more can be done to build better multimodal biological reasoning models. This is only the start.
Anshul Kundaje
@anshulkundaje
Oct 2
"Evo2 and ESM3 contribute little to BioReason and BioReason-Pro performance on the evaluated tasks. Shuffling the DNA sequence barely changes BioReason disease prediction accuracy, and in evidence conflicts the two models follow the text in 97.9% and 99.7% of cases." 1/
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Ada Fang
@AdaFang_
Oct 2
When do biological reasoning models use their biological inputs? High performance can create a mirage of reliance on biological representations. Reasoning traces don’t always reflect the evidence. Our paper examines these limits and paths to better biological reasoning models.
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Ada Fang
@AdaFang_
Oct 2
When do biological reasoning models use their biological inputs? High performance can create a mirage of reliance on biological representations. Reasoning traces don’t always reflect the evidence. Our paper examines these limits and paths to better biological reasoning models.
Marinka Zitnik and 5 others
15
Ada Fang
@AdaFang_
Sep 17
Biology does not need models that just describe the world. It needs models that predict what happens when we intervene. In @CellCellPress Focus on AI in Biology, we lay out how to build biomedical world models, from the data they need to how they should be trained and evaluated.
Marinka Zitnik and 3 others
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Ada Fang
@AdaFang_
Sep 8
Check out the new AlphaGenome atlas!
Jun Cheng
@s6juncheng
Sep 8
Prioritizing and interpreting disease-associated genetic variants remains one of the greatest challenges in human genetics. Today, we’re thrilled to introduce AlphaGenome Atlas 🧬, a genome-wide platform providing precomputed predictions for the regulatory effects of all ~9
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