Next Article in Journal
Correction: Oniciuc, E. A.; et al. The Present and Future of Whole Genome Sequencing (WGS) and Whole Metagenome Sequencing (WMS) for Surveillance of Antimicrobial Resistant Microorganisms and Antimicrobial Resistance Genes across the Food Chain. Genes 2018, 9, 268.
Previous Article in Journal
CoreProbe: A Novel Algorithm for Estimating Relative Abundance Based on Metagenomic Reads
Open AccessArticle

A Similarity Regression Fusion Model for Integrating Multi-Omics Data to Identify Cancer Subtypes

School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Author to whom correspondence should be addressed.
Genes 2018, 9(7), 314;
Received: 31 March 2018 / Revised: 8 June 2018 / Accepted: 11 June 2018 / Published: 21 June 2018
(This article belongs to the Section Technologies and Resources for Genetics)
The identification of cancer subtypes is crucial to cancer diagnosis and treatments. A number of methods have been proposed to identify cancer subtypes by integrating multi-omics data in recent years. However, the existing methods rarely consider the biases of similarity between samples and weights of different omics data in integration. More accurate and flexible integration approaches need to be developed to comprehensively investigate cancer subtypes. In this paper, we propose a simple and flexible similarity fusion model for integrating multi-omics data to identify cancer subtypes. We consider the similarity biases between samples in each omics data and predict corrected similarities between samples using a generalized linear model. We integrate the corrected similarity information from multi-omics data according to different data-view weights. Based on the integrative similarity information, we cluster patient samples into different subtype groups. Comprehensive experiments demonstrate that the proposed approach obtains more significant results than the state-of-the-art integrative methods. In conclusion, our approach provides an effective and flexible tool to investigate subtypes in cancer by integrating multi-omics data. View Full-Text
Keywords: cancer subtypes; data integration; similarity fusion cancer subtypes; data integration; similarity fusion
Show Figures

Figure 1

MDPI and ACS Style

Guo, Y.; Zheng, J.; Shang, X.; Li, Z. A Similarity Regression Fusion Model for Integrating Multi-Omics Data to Identify Cancer Subtypes. Genes 2018, 9, 314.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop