Abstract
Learning pharmacogenomic multi-relations among diseases, genes and chemicals from content-rich biomedical and biological networks can provide important guidance for drug discovery, drug repositioning and disease treatment. Most of the existing methods focus on imputing missing values in the disease-gene, disease chemical and gene-chemical pairwise relations from the observed relations instead of being designed for learning high-order disease-gene-chemical multi-relations. To achieve the goal, we propose a general tensor-based optimization framework and a scalable Graph-Regularized Tensor Completion from Observed Pairwise Relations (GT-COPR) algorithm to infer the multi-relations among the entities across multiple networks in a low-rank tensor, based on manifold regularization with the graph Laplacian of a Cartesian, tensor or strong product of the networks, and consistencies between the collapsed tensors and the observed bipartite relations. Our theoretical analyses also prove the convergence and efficiency of GT-COPR. In the experiments, the tensor fiber-wise and slice-wise evaluations demonstrate the accuracy of GT-COPR for predicting the diseasegene-chemical associations across the large-scale protein-protein interactions network, chemical structural similarity network and phenotype-based human disease network; and the validation on Genomics of Drug Sensitivity in Cancer cell line dataset shows a potential clinical application of GT-COPR for learning diseasespecific chemical-gene interactions. Statistical enrichment analysis demonstrates that GT-COPR is also capable of producing both topologically and biologically relevant disease, gene and chemical components with high significance.
Original language | English (US) |
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Title of host publication | Proceedings - 19th IEEE International Conference on Data Mining, ICDM 2019 |
Editors | Jianyong Wang, Kyuseok Shim, Xindong Wu |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 409-418 |
Number of pages | 10 |
ISBN (Electronic) | 9781728146034 |
DOIs | |
State | Published - Nov 2019 |
Event | 19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China Duration: Nov 8 2019 → Nov 11 2019 |
Publication series
Name | Proceedings - IEEE International Conference on Data Mining, ICDM |
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Volume | 2019-November |
ISSN (Print) | 1550-4786 |
Conference
Conference | 19th IEEE International Conference on Data Mining, ICDM 2019 |
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Country/Territory | China |
City | Beijing |
Period | 11/8/19 → 11/11/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Disease gene prioritization
- Drug repositioning
- Multi-relational learning
- Product graphs
- Tensor completion