AI赋能虚拟空间蛋白组学，Cell、Nature Medicine论文复现及原理全解析

Core Insights - The article emphasizes the integration of spatial omics and AI models to enhance the understanding of cellular positioning and molecular information from pathology slides, aiming to build personalized AI models for research applications [1][6]. Course Features - The course aims to teach participants how to build their own AI models, covering the entire process from task definition to model evaluation, rather than just using existing models [6]. - It includes a dual approach of replicating top-tier journal articles while understanding the underlying mechanisms and results of the models, bridging the gap between paper replication and model construction [7]. - The course facilitates a comprehensive workflow that integrates various modalities, including images, single-cell data, and spatial mapping, to develop cross-modal integration capabilities [8]. - It emphasizes the application of learned methodologies to participants' own research projects, such as predicting spatial proteins and modeling prognosis [9]. - The course format includes live teaching, recorded sessions, and ongoing one-on-one support, ensuring comprehensive learning and application [10]. Course Schedule - The course spans one and a half months with sessions held three times a week, totaling twenty-two classes, including eighteen focused lectures and four introductory programming classes [11]. Course Modules - The curriculum is divided into four main modules, starting with Python programming for beginners, followed by the replication of Nature Medicine and CELL articles, and concluding with data mining from the TCGA public database [12]. Nature Medicine Replication - The first module focuses on understanding pathology images and CODEX multi-channel data, emphasizing the efficient reading and processing of large images [15]. - Subsequent lectures cover the alignment of digital pathology images with CODEX images, ensuring accurate data integration for analysis [17]. - The course also includes training on constructing single-cell expression matrices and understanding spatial distribution patterns [19]. CELL Replication - The CELL module teaches participants how to generate virtual multi-immune fluorescence images from pathology slides, focusing on precise alignment and quality control of images [35][36]. TCGA Database Mining - The course includes training on extracting and preparing data from the TCGA database, emphasizing the importance of clinical data integration and survival analysis [40][43]. Course Outcomes - Participants will learn to build their own AI models, replicate top-tier journal methodologies, and integrate various data types into a cohesive analysis framework [59][60]. - The course aims to equip participants with practical skills to apply learned concepts to their own research, enhancing their ability to design and execute AI projects [62][63].