School of Computer Science, Fudan University, Shanghai 200433, China.
Department of Computer Science and Technology, Tongji University, Shanghai 200092, China.
Genomics Proteomics Bioinformatics. 2014 Feb;12(1):48-51. doi: 10.1016/j.gpb.2013.06.001. Epub 2013 Aug 8.
In the past decades, advances in high-throughput technologies have led to the generation of huge amounts of biological data that require analysis and interpretation. Recently, nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data as well as to interpret them, and has been applied to various fields of biological research. In this paper, we present CloudNMF, a distributed open-source implementation of NMF on a MapReduce framework. Experimental evaluation demonstrated that CloudNMF is scalable and can be used to deal with huge amounts of data, which may enable various kinds of a high-throughput biological data analysis in the cloud. CloudNMF is freely accessible at http://admis.fudan.edu.cn/projects/CloudNMF.html.
在过去的几十年中,高通量技术的进步导致了大量生物数据的产生,这些数据需要进行分析和解释。最近,非负矩阵分解(NMF)作为一种有效的数据简化和解释方法被引入,并已应用于生物研究的各个领域。在本文中,我们提出了 CloudNMF,这是一种基于 MapReduce 框架的 NMF 的分布式开源实现。实验评估表明,CloudNMF 是可扩展的,可以用于处理大量数据,这可能使各种高通量生物数据分析在云中得以实现。CloudNMF 可在 http://admis.fudan.edu.cn/projects/CloudNMF.html 上免费获取。