Journal article
Microbiome Data Representation by Joint Nonnegative Matrix Factorization with Laplacian Regularization
IEEE/ACM transactions on computational biology and bioinformatics, v 14(2), pp 353-359
Mar 2017
PMID: 28368813
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Microbiome datasets are often comprised of different representations or views which provide complementary information to understand microbial communities, such as metabolic pathways, taxonomic assignments, and gene families. Data integration methods including approaches based on nonnegative matrix factorization (NMF) combine multi-view data to create a comprehensive view of a given microbiome study by integrating multi-view information. In this paper, we proposed a novel variant of NMF which called Laplacian regularized joint non-negative matrix factorization (LJ-NMF) for integrating functional and phylogenetic profiles from HMP. We compare the performance of this method to other variants of NMF. The experimental results indicate that the proposed method offers an efficient framework for microbiome data analysis.
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Details
- Title
- Microbiome Data Representation by Joint Nonnegative Matrix Factorization with Laplacian Regularization
- Creators
- Xingpeng JiangXiaohua HuWeiwei Xu
- Publication Details
- IEEE/ACM transactions on computational biology and bioinformatics, v 14(2), pp 353-359
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE); United States
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000399013500014
- Scopus ID
- 2-s2.0-85027724746
- Other Identifier
- 991014878007904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Computer Science, Interdisciplinary Applications
- Mathematics, Interdisciplinary Applications
- Statistics & Probability