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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xingpeng Jiang Xiaohua Hu |
| Copyright Year | 2014 |
| Description | Author affiliation: Coll. of Comput. & Inf., Drexel Univ., Philadelphia, PA, USA (Xingpeng Jiang; Xiaohua Hu) |
| Abstract | Microbiome datasets are often comprised of different representations or views which provide complementary information, such as metabolic pathways, taxonomic assignments and gene families. Computational methods for integration of multi-view information combine these data to create a comprehensive view of a given microbiome study. Similarity network fusion (SNF) provides a candidate to solve this problem by efficiently fusing similarity networks built from each data view into one network that represents the full spectrum of the underlying data. Based on this method, we propose a Robust Similarity Network Fusion (RSNF) approach which combines the strength of random forest to construct robust affine graph and the advantage of SNF at data aggregation. The experimental results indicate that the proposed strategy not only substantially outperforms single data type analysis but improve the clustering performance significantly comparing to several state-of-the-art methods in various datasets. The application on human microbiome data suggests that we can cluster microbiome samples in high accuracy. |
| Starting Page | 418 |
| Ending Page | 423 |
| File Size | 4089180 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999194 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Measurement Adaptation models Heating Nonnegative matrix factorization Data integration Clustering algorithms Network diffusion Robustness Spectral clustering Kernel Microbiome |
| Content Type | Text |
| Resource Type | Article |
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