Name
Title | ||
|---|---|---|
Learning a Low-Rank Tensor of Pharmacogenomic Multi-relations from Biomedical Networks |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDM.2019.00051 | 2019 IEEE International Conference on Data Mining (ICDM) |
Keywords | Field | DocType |
multi-relational learning, drug repositioning, disease gene prioritization, product graphs, tensor completion | Convergence (routing),Pairwise comparison,Data mining,Laplacian matrix,Tensor,Computer science,Biological network,Bipartite graph,Artificial intelligence,Missing data,Machine learning,Scalability | Conference |
ISSN | ISBN | Citations |
1550-4786 | 978-1-7281-4605-8 | 0 |
PageRank | References | Authors |
0.34 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuliu Li | 1 | 5 | 2.13 |
| Wei Zhang | 2 | 201 | 20.58 |
| R Stephanie Huang | 3 | 3 | 2.37 |
| Rui Kuang | 4 | 484 | 31.16 |