Prediction of Ovarian Cancer-Related Metabolites Based on Graph Neural Network - Chen, Jingjing; Chen, Yingying; Sun, Kefeng; Wang, Yu; He, Hui; Sun, Lin; Ha, Sifu; Li, Xiaoxiao; Ou, Yifei; Zhang, Xue; Bi, Yanli (Frontiers in Cell and Developmental Biology, 2021)
Ovarian cancer is one of the three most malignant tumors of the female reproductive system. At present, researchers do not know its pathogenesis, which makes the treatment effect unsatisfactory. Metabolomics is closely related to drug efficacy, safety evaluation, mechanism of action and rational drug use. Therefore, identifying ovarian cancer-related metabolites could greatly help researchers understand the pathogenesis and develop treatment plans. However, the measurement of metabolites is inaccurate and greatly affects by environment, and biological experiment is time-consuming and costly. Therefore, researchers tend to use computational methods to identify diseases-related metabolites in large-scale. Since the hypothesis that similar diseases are related to similar metabolites is widely accepted, in this paper, we built both disease similarity network and metabolites similarity network and used Graph Convolutional Network (GCN) to encode these networks. Then, Support Vector Machine (SVM) was used to identify whether a metabolite is related to ovarian cancer. The experiment results show high accuracy of our method. Finally, we proposed an effective method to prioritise ovarian cancer-related metabolites in large scale.