Emerging artificial intelligence applications in Spatial Transcriptomics analysis.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

引用：Li Y, Stanojevic S, Garmire LX. (2022). Emerging artificial intelligence applications in Spatial Transcriptomics analysis. Computational and structural biotechnology journal. PMID 35765645 · PMC9201012 · DOI 10.1016/j.csbj.2022.05.056

证据类型：REVIEW

主题：rna-seq、spatial-omics

被引次数（采集时）：39

原始记录：[Europe PMC](https://europepmc.org/article/MED/35765645)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

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.

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