GCN-MF: Disease-gene association identification by graph convolutional networks and matrix factorization

Peng Han, Shuo Shang, Peng Yang, Yong Liu, Peilin Zhao, Jiayu Zhou, Xin Gao, Panos Kalnis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

58 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '19
PublisherAssociation for Computing Machineryacmhelp@acm.org
Number of pages9
ISBN (Print)9781450362016
StatePublished - Jul 26 2019


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