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

共 50 条

[1] Deep generative models in single-cell omics
Rivero-Garcia, Inés
Torres, Miguel
Sánchez-Cabo, Fátima
[J]. Computers in Biology and Medicine, 2024, 176
[2] Interpretable deep learning in single-cell omics
Wagle, Manoj M.
Long, Siqu
Chen, Carissa
Liu, Chunlei
Yang, Pengyi
[J]. BIOINFORMATICS, 2024, 40 (06)
[3] The performance of deep generative models for learning joint embeddings of single-cell multi-omics data
Brombacher, Eva
Hackenberg, Maren
Kreutz, Clemens
Binder, Harald
Treppner, Martin
[J]. FRONTIERS IN MOLECULAR BIOSCIENCES, 2022, 9
[4] Single-cell multimodal omics: the power of many
Zhu, Chenxu
Preissl, Sebastian
Ren, Bing
[J]. NATURE METHODS, 2020, 17 (01) : 11 - 14
[5] Ensemble deep learning of embeddings for clustering multimodal single-cell omics data
Yu, Lijia
Liu, Chunlei
Yang, Jean Yee Hwa
Yang, Pengyi
[J]. BIOINFORMATICS, 2023, 39 (06)
[6] Single-cell multimodal omics: the power of many
Chenxu Zhu
Sebastian Preissl
Bing Ren
[J]. Nature Methods, 2020, 17 : 11 - 14
[7] Machine learning for perturbational single-cell omics
Ji, Yuge
Lotfollahi, Mohammad
Wolf, F. Alexander
Theis, Fabian J.
[J]. CELL SYSTEMS, 2021, 12 (06) : 522 - 537
[8] Multimodal deep learning approaches for single-cell multi-omics data integration
Athaya, Tasbiraha
Ripan, Rony Chowdhury
Li, Xiaoman
Hu, Haiyan
[J]. BRIEFINGS IN BIOINFORMATICS, 2023, 24 (05)
[9] Clustering of single-cell multi-omics data with a multimodal deep learning method
Xiang Lin
Tian Tian
Zhi Wei
Hakon Hakonarson
[J]. Nature Communications, 13
[10] Clustering of single-cell multi-omics data with a multimodal deep learning method
Lin, Xiang
Tian, Tian
Wei, Zhi
Hakonarson, Hakon
[J]. NATURE COMMUNICATIONS, 2022, 13 (01)

← 1 2 3 4 5 →