A multi-objective optimization method for identification of module biomarkers for disease diagnosis.

Abstract Biomarker identification aims at finding a set of biological indicators that best discriminate biological samples of different phenotypes. In this paper, we take the module containing the significant disease-related genes and their interactions from biological networks as a module biomarker, and propose an evolutionary multi-objective optimization method to identify module biomarkers for disease diagnosis. To be specific, we take the classification accuracy on control and disease samples, the association with disease and the intra-link density in the module as the optimization objectives. To achieve the best performance, a novel population initiation strategy is tailored to generate dense-connected initial solutions, and a specific population update strategy is employed to direct the evolution towards the global optimums with abundant diversity. Experimental results show that our method outperforms the previous state-of-the-art disease diagnosis methods. Meantime, the detected biomarker module can reflect the basic and significant biological functions and has a great correlation with a disease phenotype.