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DANCE: a deep learning library and benchmark platform for single-cell analysis
Genome Biology ( IF 12.3 ) Pub Date : 2024-03-19 , DOI: 10.1186/s13059-024-03211-z
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

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.

中文翻译:

DANCE:用于单细胞分析的深度学习库和基准平台

DANCE 是第一个标准、通用且可扩展的基准平台,用于访问和评估众多单细胞分析任务的基准数据集范围内的计算方法。目前,DANCE 支持 3 个模块和 8 个流行任务,在 21 个基准数据集上使用 32 种最先进的方法。人们可以通过最少的努力(例如仅使用一个命令行)轻松地在主要基准数据集上重现支持的算法的结果。此外,DANCE 还为研究人员提供了一个由深度学习架构和工具组成的生态系统,以促进他们自己的模型开发。DANCE 是一个开源 Python 包,欢迎各种贡献。
更新日期:2024-03-19
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