Emerging artificial intelligence applications in Spatial Transcriptomics analysis.
Artificial intelligence
Deep learning
Machine learning
Spatial transcriptomics
Journal
Computational and structural biotechnology journal
ISSN: 2001-0370
Titre abrégé: Comput Struct Biotechnol J
Pays: Netherlands
ID NLM: 101585369
Informations de publication
Date de publication:
2022
2022
Historique:
received:
17
03
2022
revised:
28
05
2022
accepted:
28
05
2022
entrez:
29
6
2022
pubmed:
30
6
2022
medline:
30
6
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35765645
doi: 10.1016/j.csbj.2022.05.056
pii: S2001-0370(22)00212-4
pmc: PMC9201012
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
2895-2908Informations de copyright
© 2022 The Authors.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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