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Network biology has been successfully used to help reveal complex mechanismsof disease, especially cancer. On the other hand, network biology requiresin-depth knowledge to construct disease-specific networks, but our currentknowledge is very limited even with the recent advances in human cancerbiology. Deep learning has shown a great potential to address the difficultsituation like this. However, deep learning technologies conventionally usegrid-like structured data, thus application of deep learning technologies tothe classification of human disease subtypes is yet to be explored. Recently,graph based deep learning techniques have emerged, which becomes an opportunityto leverage analyses in network biology. In this paper, we proposed a hybridmodel, which integrates two key components 1) graph convolution neural network(graph CNN) and 2) relation network (RN). We utilize graph CNN as a componentto learn expression patterns of cooperative gene community, and RN as acomponent to learn associations between learned patterns. The proposed model isapplied to the PAM50 breast cancer subtype classification task, the standardbreast cancer subtype classification of clinical utility. In experiments ofboth subtype classification and patient survival analysis, our proposed methodachieved significantly better performances than existing methods. We believethat this work is an important starting point to realize the upcomingpersonalized medicine.

Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification