a comparison of graph and kernelbased omics data integration algorithms for classifying complex traits background highthroughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health wellstudied algorithms mostly deal with single data source and cannot fully utilize the potential of these multiomics 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 lackingresults in this paper we focus on two common classes of integration algorithms graphbased that depict relationships with subjects denoted by nodes and relationships denoted by edges and kernelbased 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 graphbased semisupervised learning graph sharpening integration composite association network bayesian network semidefinite programmingsupport vector machine sdpsvm relevance vector machine rvm and adaboost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study in general kernelbased algorithms create more complex models and require longer computation time but they tend to perform better than graphbased algorithms the performance of graphbased algorithms has the advantage of being faster computationallyconclusions the empirical results demonstrate that composite association network relevance vector machine and adaboost rvm are the better performers we provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources