Khan Shawez, Kim Jong Joo
Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea.
National Center for Cancer Immune Therapy (CCIT-DK), Department of Oncology, Copenhagen University Hospital, Herlev, Denmark.
Drug Discov Today. 2024 Mar;29(3):103889. doi: 10.1016/j.drudis.2024.103889. Epub 2024 Jan 18.
Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging and transcriptomic data to enable the high-throughput analysis of the spatial localization of transcripts in diverse biological systems. The rapid progress in this field necessitates the development of innovative computational methods to effectively tackle the distinct challenges posed by the analysis of ST data. These platforms, integrating AI techniques, offer a promising avenue for understanding disease mechanisms and expediting drug discovery. Despite significant advances in the development of ST data analysis techniques, there is an ongoing need to enhance these models for increased biological relevance. In this review, we briefly discuss the ST-related databases and current deep-learning-based models for spatial transcriptome data analyses and highlight their roles and future perspectives in biomedical applications.
空间转录组学（ST）是一个新兴领域，它整合了高分辨率成像和转录组数据，以实现对不同生物系统中转录本空间定位的高通量分析。该领域的快速发展需要开发创新的计算方法，以有效应对ST数据分析带来的独特挑战。这些整合了人工智能技术的平台为理解疾病机制和加速药物发现提供了一条有前景的途径。尽管ST数据分析技术取得了重大进展，但仍需要不断改进这些模型，以提高其生物学相关性。在这篇综述中，我们简要讨论了与ST相关的数据库以及当前基于深度学习的空间转录组数据分析模型，并强调了它们在生物医学应用中的作用和未来前景。
