Zi-Chao Zhang1,2, Xiao-Fei Zhang3, Min Wu4, Le Ou-Yang1, Xing-Ming Zhao2,5, Xiao-Li Li4. 1. Guangdong Key Laboratory of Intelligent Information Processing, Key Laboratory of Media Security, Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen 518060, China. 2. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China. 3. School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China. 4. Institute for Infocomm Research (I2R), A*STAR, 138632, Singapore. 5. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, 200433 China.
Abstract
MOTIVATION: Predicting potential links in biomedical bipartite networks can provide useful insights into the diagnosis and treatment of complex diseases and the discovery of novel drug targets. Computational methods have been proposed recently to predict potential links for various biomedical bipartite networks. However, existing methods are usually rely on the coverage of known links, which may encounter difficulties when dealing with new nodes without any known link information. RESULTS: In this study, we propose a new link prediction method, named graph regularized generalized matrix factorization (GRGMF), to identify potential links in biomedical bipartite networks. First, we formulate a generalized matrix factorization model to exploit the latent patterns behind observed links. In particular, it can take into account the neighborhood information of each node when learning the latent representation for each node, and the neighborhood information of each node can be learned adaptively. Second, we introduce two graph regularization terms to draw support from affinity information of each node derived from external databases to enhance the learning of latent representations. We conduct extensive experiments on six real datasets. Experiment results show that GRGMF can achieve competitive performance on all these datasets, which demonstrate the effectiveness of GRGMF in prediction potential links in biomedical bipartite networks. AVAILABILITY AND IMPLEMENTATION: The package is available at https://github.com/happyalfred2016/GRGMF. CONTACT: leouyang@szu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Predicting potential links in biomedical bipartite networks can provide useful insights into the diagnosis and treatment of complex diseases and the discovery of novel drug targets. Computational methods have been proposed recently to predict potential links for various biomedical bipartite networks. However, existing methods are usually rely on the coverage of known links, which may encounter difficulties when dealing with new nodes without any known link information. RESULTS: In this study, we propose a new link prediction method, named graph regularized generalized matrix factorization (GRGMF), to identify potential links in biomedical bipartite networks. First, we formulate a generalized matrix factorization model to exploit the latent patterns behind observed links. In particular, it can take into account the neighborhood information of each node when learning the latent representation for each node, and the neighborhood information of each node can be learned adaptively. Second, we introduce two graph regularization terms to draw support from affinity information of each node derived from external databases to enhance the learning of latent representations. We conduct extensive experiments on six real datasets. Experiment results show that GRGMF can achieve competitive performance on all these datasets, which demonstrate the effectiveness of GRGMF in prediction potential links in biomedical bipartite networks. AVAILABILITY AND IMPLEMENTATION: The package is available at https://github.com/happyalfred2016/GRGMF. CONTACT: leouyang@szu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Varun S Sharma; Andrea Fossati; Rodolfo Ciuffa; Marija Buljan; Evan G Williams; Zhen Chen; Wenguang Shao; Patrick G A Pedrioli; Anthony W Purcell; María Rodríguez Martínez; Jiangning Song; Matteo Manica; Ruedi Aebersold; Chen Li Journal: Brief Bioinform Date: 2022-07-18 Impact factor: 13.994