Journal article
Multi-View Clustering of Microbiome Samples by Robust Similarity Network Fusion and Spectral Clustering
IEEE/ACM transactions on computational biology and bioinformatics, v 14(2), pp 264-271
Mar 2017
PMID: 26513798
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Microbiome datasets are often comprised of different representations or views which provide complementary information, such as genes, functions, and taxonomic assignments. Integration of multi-view information for clustering microbiome samples could create a comprehensive view of a given microbiome study. Similarity network fusion (SNF) can efficiently integrate similarities built from each view of data into a unique network that represents the full spectrum of the underlying data. Based on this method, we develop a Robust Similarity Network Fusion (RSNF) approach which combines the strength of random forest and the advantage of SNF at data aggregation. The experimental results indicate the strength of the proposed strategy. The method substantially improves the clustering performance significantly comparing to several state-of-the-art methods in several datasets.
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Details
- Title
- Multi-View Clustering of Microbiome Samples by Robust Similarity Network Fusion and Spectral Clustering
- Creators
- Yong ZhangXiaohua HuXingpeng Jiang
- Publication Details
- IEEE/ACM transactions on computational biology and bioinformatics, v 14(2), pp 264-271
- 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:000399013500004
- Scopus ID
- 2-s2.0-85029201893
- Other Identifier
- 991014878634204721
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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