Li Yijun, Stanojevic Stefan, Garmire Lana X
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Comput Struct Biotechnol J. 2022 Jun 2;20:2895-2908. doi: 10.1016/j.csbj.2022.05.056. eCollection 2022.
Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis. This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.
空间转录组学（ST）在过去几年中有了显著进展。这种进展伴随着对新型计算方法的迫切需求，以应对ST数据分析的独特挑战。已经开发了许多人工智能（AI）方法，利用各种机器学习和深度学习技术进行ST的计算分析。本文综述对当前用于ST分析的AI方法进行了全面且最新的调查。
