Diagnosing schizophrenia by integrating genomic and imaging data through network fusion

In order to increase the accuracy for the diagnosis of schizophrenia (SCZ) disease, it is essential to integratively employ complementary information from multiple types of data. It is well known that a network is a graph based method for analyzing relationships between patients, with its nodes and edges representing patients and their relationships respectively. In this study, we developed a network-based prediction approach by taking advantage of fused network from multiple data types rather than individual networks. Specifically, we constructed a fused network using three types of data including genetic, epigenetic and neuroimaging data from the study of schizophrenia. The majority neighborhood of a node in the network was exploited for discriminating SCZ from healthy controls. In comparison with other 9 graph-based label prediction methods, our prediction method shows the best performance according to several metrics. The prediction power of our proposed method was also tested with different parameters and optimal parameters were determined. We show that the label prediction method based on network fusion from multiple data types shows promises for more accurate diagnosis of schizophrenia, which can also be extended to other disease models.