MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis.

BMC Bioinformatics
Yuanyuan MaYingjun Ma

Abstract

With the rapid development of high-throughput technique, multiple heterogeneous omics data have been accumulated vastly (e.g., genomics, proteomics and metabolomics data). Integrating information from multiple sources or views is challenging to obtain a profound insight into the complicated relations among micro-organisms, nutrients and host environment. In this paper we propose a multi-view Hessian regularization based symmetric nonnegative matrix factorization algorithm (MHSNMF) for clustering heterogeneous microbiome data. Compared with many existing approaches, the advantages of MHSNMF lie in: (1) MHSNMF combines multiple Hessian regularization to leverage the high-order information from the same cohort of instances with multiple representations; (2) MHSNMF utilities the advantages of SNMF and naturally handles the complex relationship among microbiome samples; (3) uses the consensus matrix obtained by MHSNMF, we also design a novel approach to predict the classification of new microbiome samples. We conduct extensive experiments on two real-word datasets (Three-source dataset and Human Microbiome Plan dataset), the experimental results show that the proposed MHSNMF algorithm outperforms other baseline and state-of-the-art ...Continue Reading

References

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Sep 30, 2017·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Yuanyuan MaXingpeng Jiang

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Methods Mentioned

BETA
phylogenetic profile

Software Mentioned

MHSNMF
SNMF
Metagenomics of ( MetaHIT )
MetaPhlAn

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