Tightly integrated genomic and epigenomic data mining using tensor decomposition

• Published in: Bioinformatics Vol. 35; no. 1; pp. 112 - 118
• Main Author: Fang, Jianwen
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.01.2019
• Subjects: Computational Biology; Data Mining; DNA Copy Number Variations; DNA Methylation; Epigenomics; Genomics; Humans; Neoplasms; Original Papers; RNA, Messenger
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/bty513

Abstract Motivation Complex diseases such as cancers often involve multiple types of genomic and/or epigenomic abnormalities. Rapid accumulation of multiple types of omics data demands methods for integrating the multidimensional data in order to elucidate complex relationships among different types of genomic and epigenomic abnormalities. Results In the present study, we propose a tightly integrated approach based on tensor decomposition. Multiple types of data, including mRNA, methylation, copy number variations and somatic mutations, are merged into a high-order tensor which is used to develop predictive models for overall survival. The weight tensors of the models are constrained using CANDECOMP/PARAFAC (CP) tensor decomposition and learned using support tensor machine regression (STR) and ridge tensor regression (RTR). The results demonstrate that the tensor decomposition based approaches can achieve better performance than the models based individual data type and the concatenation approach. Supplementary information Supplementary data are available at Bioinformatics online.