DANCE : a deep learning library and benchmark platform for single-cell analysis
© 2024. The Author(s)..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:25 |
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| Enthalten in: |
Genome biology - 25(2024), 1 vom: 19. März, Seite 72 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ding, Jiayuan [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 21.03.2024 Date Revised 23.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s13059-024-03211-z |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369939344 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Benchmarking | |
| 650 | 4 | |a Cell type annotation | |
| 650 | 4 | |a Cell type deconvolution | |
| 650 | 4 | |a Clustering | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Gene imputation | |
| 650 | 4 | |a Multimodality integration | |
| 650 | 4 | |a Single-cell multimodal analysis | |
| 650 | 4 | |a Single-cell spatial analysis | |
| 650 | 4 | |a Spatial domain identification | |
| 700 | 1 | |a Liu, Renming |e verfasserin |4 aut | |
| 700 | 1 | |a Wen, Hongzhi |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Wenzhuo |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhaoheng |e verfasserin |4 aut | |
| 700 | 1 | |a Venegas, Julian |e verfasserin |4 aut | |
| 700 | 1 | |a Su, Runze |e verfasserin |4 aut | |
| 700 | 1 | |a Molho, Dylan |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yixin |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Qiaolin |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Lingxiao |e verfasserin |4 aut | |
| 700 | 1 | |a Zuo, Wangyang |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Yuying |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Jiliang |e verfasserin |4 aut | |
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