YC Paper Club 本期聚焦 AI 与生物学交叉研究
YC 请来四位研究者聊 AI 怎么读基因、建虚拟细胞，还有 DNA 语言模型的 inside 拆解，做 AI Bio 的朋友值得收藏。
Y Combinator 本期 Paper Club 围绕 AI for Biology 展开，核心观点是测序成本大幅下降后，生物数据量已超出研究人员消化能力。内容覆盖癌症基因组学、虚拟细胞、基因组语言模型和生物设计四个方向。其中 Garyk Brixi 介绍了在 DNA 上训练的语言模型，并讨论了模型在未被显式训练的情况下如何涌现出 in-context learning 能力。Divya Nori 分享了用 few-shot 提示词做生物设计的实操方法。
This week’s Paper Club is all about AI for biology. Sequencing costs have collapsed, biology is generating more data than researchers can make sense of, and the models are beginning to catch up. The opportunity now is to move from reading biological data to modeling and designing living systems. We brought together researchers and builders working across cancer genomics, virtual cells, genome language models, and biological design to show what that shift looks like in practice. We start with a single patient, asking how AI can build a research program around their cancer, then zoom out to whole cells and genomes. Along the way, we look inside a language model trained on DNA and explore how in-context learning emerged even though the model was never explicitly trained to do it. 00:00 - Is the Turing test solved? A live experiment 03:22 - Why now is the best time for AI Bio 08:26 - Alfredo Gonzalez: cancer as a data problem 29:50 - Yusuf Roohani: virtual cells and autonomous research 51:47 - Garyk Brixi: what a model learns from DNA 1:09:57 - Divya Nori: few-shot prompting for biological design Your browser does not support the video tag. 🔗 View on Twitter 💬 7 🔄 5 ❤️ 20 👀 5146 📊 9 ⚡
