A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.

ABSTRACT:

Background

High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking.

Results

In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based tha

SUBMITTER: Yan KK 

PROVIDER: S-EPMC6389230 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature