催交
数据科学
计算机科学
领域(数学)
药物发现
药物开发
空间分析
相关性(法律)
信息基础设施
透视图(图形)
计算生物学
人工智能
生物信息学
生物
工程类
系统工程
地理
药理学
遥感
法学
纯数学
数学
政治学
药品
作者
Mohd Danishuddin,Shawez Khan,Jong-Joo Kim
标识
DOI:10.1016/j.drudis.2024.103889
摘要
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.
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