衰老研究实验室用 Co-Scientist 提速：从文献提出 20 多个候选遗传因子、部分经实验验证可让细胞返年轻态，把半年的数据分析压到几天
Fast-tracking genetic leads to reverse cellular aging
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Google DeepMind 博客：生物学家 Omar Abudayyeh 与 Jonathan Gootenberg 的实验室用 Co-Scientist 突破衰老研究的两大瓶颈——决定测哪些遗传通路、以及读懂海量筛选数据。他们的实验室批量开关上千基因、读取细胞响应，目标是找出让细胞脱离与衰老相关的 senescence 损伤态、回到更年轻状态（皮肤、毛发、肌肉等组织）。Co-Scientist 一是提线索：检索数万篇论文、权衡多种假设，提出 20 多个新颖且合理的遗传因子，实验已验证其中若干条确能把细胞推向更年轻、功能更好的状态；二是加速收尾：把筛选数据连同文献一起分析，原本可能耗时六个月的解读被压缩到几天。
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Google DeepMind#论文/研究#科研AI
正文
Two of the biggest bottlenecks in aging research are deciding which genetic pathways to test and making sense of the vast data those experiments produce. Biologists Omar Abudayyeh and Jonathan Gootenberg are using Co-Scientist to help them blast through both.
Their lab runs huge genetic screens that flip thousands of genes on or off then reads how cells respond to these changes. The goal is to find changes that push cells away from senescence – a damaged state linked to aging – and toward a youthful state in tissues such as skin, hair, and muscle.
Co-Scientist is helping on two fronts. First, it generates leads. When the team asked it to trawl the scientific literature for factors that might reverse aging, it scanned tens of thousands of papers, considered a multitude of hypotheses, and ultimately proposed more than 20 novel, plausible genetic factors to test. Lab tests validated a couple Co-Scientist’s hypotheses, with its recommended factors successfully driving cells into a younger state with improved overall function.
Second, Co-Scientist speeds up the follow-through. Once the team has results from a big screen, they have to figure out what the enormous amount of data might mean, and which directions are worth pursuing next. That kind of analysis – trying to connect test results to years of scattered scientific literature – can take a researcher up to six months. Having Co-Scientist analyse their screening data alongside the literature, that work is slashed to just a few days.