Emerging Artificial Intelligence Applications in Spatial Transcriptomics Analysis
Yijun Li, Stefan Stanojevic, Lana X. Garmire
TL;DR
This paper reviews recent advances in artificial intelligence methods, including machine learning and deep learning, applied to spatial transcriptomics data analysis, highlighting the growing importance of AI in this rapidly evolving field.
Contribution
It provides a comprehensive, up-to-date survey of AI techniques specifically tailored for spatial transcriptomics analysis, emphasizing recent methodological developments.
Findings
AI methods enhance spatial transcriptomics data analysis
Deep learning models improve spatial resolution and gene expression interpretation
Survey highlights key challenges and future directions in AI for ST
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.
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