{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"萌喵读文献-生物信息学","title":"今日生物信息学最高分文献 - 2026-03-29","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/cd55e891\"></iframe>","width":"100%","height":180,"duration":148,"description":"【科研喵使用AI读文献，祝你效率百倍，访问labcat.com.cn下载】\n\n本期关注发表在Briefings in bioinformatics(影响因子6.8)上的重要综述《Toward next-generation machine learning and deep learning for spatial omics》。这项研究深入探讨了空间组学技术中机器学习与深度学习的最新进展，对比了传统机器学习方法与深度学习架构在批效应校正、组织分割、空间域发现等任务中的表现。研究指出，深度学习能更好地捕捉复杂空间模式，但仍面临数据稀缺和计算成本等挑战。文章提出了一个决策框架，帮助研究者根据数据模态、空间分辨率和组织结构选择最适合的模型，为空间组学数据的可重复、可解释和临床转化应用提供了重要指导，将加速精准医学发展。","thumbnail_url":"https://img.transistorcdn.com/38WU46uKrju37cyiLrKcVSktFL4vxBb_oTgbpM_2CRw/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8zOGU2/ZmEyYWE0MDZkNGFj/NWMzNGY5ZmU4YTk0/ZTBlNS5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}