GCN-MF: Disease-gene association identification by graph convolutional networks and matrix factorization
Type
Conference PaperKAUST Department
King Abdullah University of Science and TechnologyComputer Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2019-07-26Online Publication Date
2019-07-26Print Publication Date
2019Permanent link to this record
http://hdl.handle.net/10754/656764
Metadata
Show full item recordAbstract
Discovering disease-gene association is a fundamental and critical biomedical task, which assists biologists and physicians to discover pathogenic mechanism of syndromes. With various clinical biomarkers measuring the similarities among genes and disease phenotypes, network-based semi-supervised learning (NSSL) has been commonly utilized by these studies to address this class-imbalanced large-scale data issue. However, most existing NSSL approaches are based on linear models and suffer from two major limitations: 1) They implicitly consider a local-structure representation for each candidate; 2) They are unable to capture nonlinear associations between diseases and genes. In this paper, we propose a new framework for disease-gene association task by combining Graph Convolutional Network (GCN) and matrix factorization, named GCN-MF. With the help of GCN, we could capture nonlinear interactions and exploit measured similarities. Moreover, we define a margin control loss function to reduce the effect of sparsity. Empirical results demonstrate that the proposed deep learning algorithm outperforms all other state-of-the-art methods on most of metrics.Citation
Han, P., Yang, P., Zhao, P., Shang, S., Liu, Y., Zhou, J., … Kalnis, P. (2019). GCN-MF. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi:10.1145/3292500.3330912Conference/Event name
25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019Additional Links
http://dl.acm.org/citation.cfm?doid=3292500.3330912ae974a485f413a2113503eed53cd6c53
10.1145/3292500.3330912