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

Published

Journal Article

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 that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. CONCLUSIONS:The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

Full Text

Duke Authors

Cited Authors

  • Yan, KK; Zhao, H; Pang, H

Published Date

  • December 6, 2017

Published In

Volume / Issue

  • 18 / 1

Start / End Page

  • 539 -

PubMed ID

  • 29212468

Pubmed Central ID

  • 29212468

Electronic International Standard Serial Number (EISSN)

  • 1471-2105

International Standard Serial Number (ISSN)

  • 1471-2105

Digital Object Identifier (DOI)

  • 10.1186/s12859-017-1982-4

Language

  • eng