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DANCE: a deep learning library and benchmark platform for single-cell analysis

Authors :
Jiayuan Ding
Renming Liu
Hongzhi Wen
Wenzhuo Tang
Zhaoheng Li
Julian Venegas
Runze Su
Dylan Molho
Wei Jin
Yixin Wang
Qiaolin Lu
Lingxiao Li
Wangyang Zuo
Yi Chang
Yuying Xie
Jiliang Tang
Source :
Genome Biology, Vol 25, Iss 1, Pp 1-28 (2024)
Publication Year :
2024
Publisher :
BMC, 2024.

Abstract

Abstract DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

Details

Language :
English
ISSN :
1474760X
Volume :
25
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Genome Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.82df36dc168a46dcab46d25ab356cce5
Document Type :
article
Full Text :
https://doi.org/10.1186/s13059-024-03211-z