ArticleGenomics, proteomics & bioinformatics2014
CloudNMF: a MapReduce implementation of nonnegative matrix factorization for large-scale biological datasets.
Article in Genomics, proteomics & bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data.Interdisciplinary sciences, computational life sciences · 2022Article
- Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform.Scientific reports · 2018Article
- Enter the Matrix: Factorization Uncovers Knowledge from Omics.Trends in genetics : TIG · 2018Review
- Article
- NMF-mGPU: non-negative matrix factorization on multi-GPU systems.BMC bioinformatics · 2015Article
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Authors and funding
4 authors.
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Abstract
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.
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