AI+生信，在CNS顶刊论文的应用

Core Viewpoint - The article emphasizes the integration of AI tools, particularly DeepSeek, in enhancing research methodologies in bioinformatics, specifically in the analysis of multi-omics data and the design of research projects [1][3][29]. Course Structure - The course includes practical sessions on AI-assisted design of research topics, focusing on multi-omics data analysis, including metabolomics, proteomics, and genomics [3][30]. - It covers the use of AI for efficient reading and summarizing of CNS literature, as well as the evaluation of innovative ideas and data analysis feasibility in bioinformatics [3][29]. AI Applications in Bioinformatics - AI is utilized for sample grouping, data filtering from public databases, and visualizing bioinformatics analysis results [4][19]. - The course includes modules on machine learning applications in metabolomics, proteomics, and transcriptomics, highlighting various algorithms and their implementations [33][40]. Research Methodologies - The curriculum emphasizes the importance of understanding and applying various statistical and machine learning methods for data analysis, including regression models and clustering techniques [44][40]. - It also includes practical coding sessions to replicate high-level research from CNS articles, enhancing hands-on experience [50][51]. Continuous Support and Learning - The program offers ongoing support and one-on-one guidance even after course completion, ensuring that participants can effectively apply their learning in real-world scenarios [22][24][50]. - The course is designed to be flexible, allowing participants to revisit materials and receive assistance as needed [22][26].