CloudNMF: a MapReduce implementation of nonnegative matrix factorization for large-scale biological datasets.
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.
School of Computer Science, Fudan University, Shanghai 200433, China.
Ruiqi
R
Liao
School of Computer Science, Fudan University, Shanghai 200433, China.
Yifan
Y
Zhang
Department of Computer Science and Technology, Tongji University, Shanghai 200092, China.
Jihong
J
Guan
School of Computer Science, Fudan University, Shanghai 200433, China; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China. Electronic address: sgzhou@fudan.edu.cn.
Shuigeng
S
Zhou
D000465:Algorithms; D001695:Biology / Q000379:methods*; D016208:Databases, Factual; D020407:Internet; D012984:Software
Bioinformatics; MapReduce; Nonnegative matrix factorization