AI赋能，顶刊不愁：机器学习分析代谢组/蛋白组/宏基因/16S/网络药理学/转录组

Core Viewpoint - The article emphasizes the integration of AI and multi-omics analysis, highlighting the importance of machine learning in biological data analysis and the educational offerings to enhance skills in this area [1][2][3]. Group 1: Course Features - The course is designed for beginners, providing a comprehensive introduction to R programming for bioinformatics analysis [1]. - It covers various popular directions in multi-omics research, including metabolomics, proteomics, microbiomics, and transcriptomics, keeping pace with scientific advancements [1]. - The teaching model includes one-on-one guidance and a flexible learning pace with live classes and recorded sessions [3]. Group 2: Course Content Overview - The first session focuses on interpreting CNS papers using Deepseek for efficient reading and summarizing multi-omics data analysis methods [2]. - Subsequent sessions cover the design of multi-omics research projects, programming basics in R, and machine learning applications in metabolomics, proteomics, and microbiomics [2][4][6]. - Advanced topics include the use of various machine learning models like xgboost, lasso, and random forests for intelligent data analysis [3][10]. Group 3: Practical Applications - The course includes hands-on experience with real CNS article source codes, allowing participants to replicate high-level research [3]. - It emphasizes the application of machine learning techniques in analyzing metabolomics and proteomics data, including regression models and network analysis [4][9]. - The integration of multi-omics data for comprehensive analysis is highlighted, showcasing the potential for significant insights in biomedical research [12][14].