Emerging artificial intelligence applications in Spatial Transcriptomics analysis

Yijun Li; Stefan Stanojevic; Lana X Garmire

doi:10.1016/j.csbj.2022.05.056

Emerging artificial intelligence applications in Spatial Transcriptomics analysis

Comput Struct Biotechnol J. 2022 Jun 2:20:2895-2908. doi: 10.1016/j.csbj.2022.05.056. eCollection 2022.

Authors

Yijun Li¹, Stefan Stanojevic², Lana X Garmire^{1

2}

Affiliations

¹ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
² Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Abstract

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.

Keywords: Artificial intelligence; Deep learning; Machine learning; Spatial transcriptomics.

Publication types

Review