Harnessing the deep learning power of foundation models in single-cell omics

被引：0

作者：

Ma, Qin ^{[1
,2
]}

Jiang, Yi ^{[1
]}

Cheng, Hao ^{[1
]}

Xu, Dong ^{[3
]}

机构：

[1] Ohio State Univ, Coll Med, Dept Biomed Informat, Columbus, OH 43210 USA

[2] Ohio State Univ, Pelotonia Inst Immuno Oncol, James Comprehens Canc Ctr, Columbus, OH 43210 USA

[3] Univ Missouri, Bond Life Sci Ctr, Dept Elect Engn & Comp Sci, Columbia, MO USA

来源：

NATURE REVIEWS MOLECULAR CELL BIOLOGY | 2024年 / 25卷 / 08期

关键词：

D O I：

10.1038/s41580-024-00756-6

中图分类号：

Q2 [细胞生物学];

学科分类号：

071009 ; 090102 ;

摘要：

Foundation models hold great promise for analyzing single-cell omics data, yet various challenges remain that require further advancements. In this Comment, we discuss the progress, limitations and best practices in applying foundation models to interrogate data and improve downstream tasks in single-cell omics. This Comment discusses the progress, limitations and best practices in applying foundation models to single-cell omics data.

引用

页码：593 / 594

页数：2

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