Large-language models facilitate discovery of the molecular signatures regulating sleep and activity

大语言模型有助于发现调节睡眠和活动的分子特征

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作者:Di Peng #, Liubin Zheng #, Dan Liu #, Cheng Han #, Xin Wang, Yan Yang, Li Song, Miaoying Zhao, Yanfeng Wei, Jiayi Li, Xiaoxue Ye, Yuxiang Wei, Zihao Feng, Xinhe Huang, Miaomiao Chen, Yujie Gou, Yu Xue, Luoying Zhang

Abstract

Sleep, locomotor and social activities are essential animal behaviors, but their reciprocal relationships and underlying mechanisms remain poorly understood. Here, we elicit information from a cutting-edge large-language model (LLM), generative pre-trained transformer (GPT) 3.5, which interprets 10.2-13.8% of Drosophila genes known to regulate the 3 behaviors. We develop an instrument for simultaneous video tracking of multiple moving objects, and conduct a genome-wide screen. We have identified 758 fly genes that regulate sleep and activities, including mre11 which regulates sleep only in the presence of conspecifics, and NELF-B which regulates sleep regardless of whether conspecifics are present. Based on LLM-reasoning, an educated signal web is modeled for understanding of potential relationships between its components, presenting comprehensive molecular signatures that control sleep, locomotor and social activities. This LLM-aided strategy may also be helpful for addressing other complex scientific questions.

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