Nature子刊：华中科技大学薛宇/彭迪团队开发结合深度学习和大语言模型的组学解读工作流

Core Viewpoint - The research published by Huazhong University of Science and Technology introduces a hybrid workflow named LyMOI, which combines deep learning and large language models to enhance the understanding of autophagy regulatory factors and discover new cancer therapies [2][5]. Group 1: Research Methodology - The LyMOI workflow integrates GPT-3.5 for biological knowledge reasoning and employs a large graph model based on graph convolutional networks (GCN) [5]. - The model incorporates evolutionarily conserved protein interactions and utilizes hierarchical fine-tuning techniques to predict molecular regulatory factors from multi-omics data [5]. Group 2: Research Findings - The LyMOI system analyzed 1.3TB of transcriptomic, proteomic, and phosphoproteomic data, expanding the understanding of autophagy regulatory factors [7]. - It accurately identified two human cancer proteins, CTSL and FAM98A, which enhance autophagy effects under the treatment of the anti-tumor agent disulfiram (DSF) [7]. - In vitro experiments indicated that silencing these two genes weakened DSF-mediated autophagy and inhibited cancer cell proliferation [7]. - Notably, the combination of DSF with the CTSL-specific inhibitor Z-FY-CHO significantly suppressed tumor growth in vivo [7].