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

BMC Bioinformatics

Research Article

Hongyu Zhao¹  Herbert Pang²  Kang K. Yan² 

[1] Department of Biostatistics, Yale University, New Haven, CT, USA;School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China;

关键词: Bayesian network;    Relevance vector machine;    Graph-based semi-supervised learning;    Semi-definite programming (SDP)-support vector machine;    Multiple data sources;    Classification;   

DOI  :  10.1186/s12859-017-1982-4

 received in 2017-06-05, accepted in 2017-11-26,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundHigh-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.ResultsIn 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.ConclusionsThe 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.

【 授权许可】

CC BY   
© The Author(s). 2017

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