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

• Published in: BMC bioinformatics Vol. 18; no. 1; pp. 539 - 13
• Main Authors: Yan, Kang K., Zhao, Hongyu, Pang, Herbert
• Format: Journal Article
• Language: English
• Published: London BioMed Central 06.12.2017; BioMed Central Ltd; BMC
• Subjects: Algorithms; Bayes Theorem; Bayesian network; Bioinformatics; Biomedical and Life Sciences; Classification; Comparative analysis; Computational Biology; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Graph-based semi-supervised learning; Humans; Hypertension; Life Sciences; Medical research; Medicine, Experimental; Microarrays; Multiple data sources; Relevance vector machine; Research Article; Semi-definite programming (SDP)-support vector machine; Support Vector Machine; Transcriptome analysis; Graph-based semi-supervised learning; Semi-definite programming (SDP)-support vector machine; Multiple data sources; Bayesian network; Relevance vector machine; Classification
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1982-4

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.