Learning a Low-Rank Tensor of Pharmacogenomic Multi-relations from Biomedical Networks

Zhuliu Li, Wei Zhang, R. Stephanie Huang, Rui Kuang

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

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 languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages409-418
Number of pages10
ISBN (Electronic)9781728146034
DOIs
StatePublished - Nov 2019
Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
Duration: Nov 8 2019Nov 11 2019

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2019-November
ISSN (Print)1550-4786

Conference

Conference19th IEEE International Conference on Data Mining, ICDM 2019
CountryChina
CityBeijing
Period11/8/1911/11/19

Keywords

  • Disease gene prioritization
  • Drug repositioning
  • Multi-relational learning
  • Product graphs
  • Tensor completion

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  • Cite this

    Li, Z., Zhang, W., Huang, R. S., & Kuang, R. (2019). Learning a Low-Rank Tensor of Pharmacogenomic Multi-relations from Biomedical Networks. In J. Wang, K. Shim, & X. Wu (Eds.), Proceedings - 19th IEEE International Conference on Data Mining, ICDM 2019 (pp. 409-418). [8970888] (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2019-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2019.00051