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DANCE: a deep learning library and benchmark platform for single-cell analysis.
Ding, Jiayuan; Liu, Renming; Wen, Hongzhi; Tang, Wenzhuo; Li, Zhaoheng; Venegas, Julian; Su, Runze; Molho, Dylan; Jin, Wei; Wang, Yixin; Lu, Qiaolin; Li, Lingxiao; Zuo, Wangyang; Chang, Yi; Xie, Yuying; Tang, Jiliang.
Afiliação
  • Ding J; Department of Computer Science and Engineering, Michigan State University, East Lansing, USA. dingjia5@msu.edu.
  • Liu R; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA.
  • Wen H; Department of Computer Science and Engineering, Michigan State University, East Lansing, USA.
  • Tang W; Department of Statistics and Probability, Michigan State University, East Lansing, USA.
  • Li Z; Department of Biostatistics, University of Washington, Seattle, USA.
  • Venegas J; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA.
  • Su R; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA.
  • Molho D; Department of Statistics and Probability, Michigan State University, East Lansing, USA.
  • Jin W; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA.
  • Wang Y; Department of Computer Science and Engineering, Michigan State University, East Lansing, USA.
  • Lu Q; Department of Bioengineering, Stanford University, Palo Alto, USA.
  • Li L; School of Artificial Intelligence, Jilin University, Jilin, China.
  • Zuo W; Department of Computer Science, Boston University, Boston, USA.
  • Chang Y; Department of Computer Science, Zhejiang University of Technology, Zhejiang, China.
  • Xie Y; School of Artificial Intelligence, Jilin University, Jilin, China.
  • Tang J; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA. xyy@msu.edu.
Genome Biol ; 25(1): 72, 2024 03 19.
Article em En | MEDLINE | ID: mdl-38504331
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Benchmarking / Aprendizado Profundo Limite: Humans Idioma: En Revista: Genome Biol Assunto da revista: BIOLOGIA MOLECULAR / GENETICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Benchmarking / Aprendizado Profundo Limite: Humans Idioma: En Revista: Genome Biol Assunto da revista: BIOLOGIA MOLECULAR / GENETICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos
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