2024
Broadbent, Charles; Song, Tianci; Kuang, Rui
Deciphering High-order Structures in Spatial Transcriptomes with Graph-guided Tucker Decomposition Best Paper Journal Article
In: Bioinformatics (Supplement Issue of the Proceedings of 32nd International Conference on Intelligent Systems for Molecular Biology (ISMB))↩, vol. 40, no. Supplement_1, pp. i529-i538, 2024.
Links | BibTeX | Tags: Multi-relational learning, Spatial Transcriptomics, Tensor Completion
@article{GraphTucker,
title = {Deciphering High-order Structures in Spatial Transcriptomes with Graph-guided Tucker Decomposition},
author = {Charles Broadbent and Tianci Song and Rui Kuang},
url = {https://academic.oup.com/bioinformatics/article/40/Supplement_1/i529/7700901},
doi = {doi.org/10.1093/bioinformatics/btae245},
year = {2024},
date = {2024-07-12},
urldate = {2024-07-12},
journal = {Bioinformatics (Supplement Issue of the Proceedings of 32nd International Conference on Intelligent Systems for Molecular Biology (ISMB))↩},
volume = {40},
number = {Supplement_1},
pages = {i529-i538},
howpublished = {To appear In the Proceedings of International Conference on Intelligent Systems for Molecular Biology (ISMB) 2024},
keywords = {Multi-relational learning, Spatial Transcriptomics, Tensor Completion},
pubstate = {published},
tppubtype = {article}
}
2021
Li, Zhuliu; Petegrosso, Raphael; Smith, Shaden; Sterling, David; Karypis, George; Kuang, Rui
Scalable Label Propagation for Multi-relational Learning on the Tensor Product of Graphs Journal Article
In: IEEE Transactions on Knowledge and Data Engineering, 2021.
Links | BibTeX | Tags: Multi-relational learning, Protein-Protein Interaction Network, Tensor Completion
@article{li2021scalable,
title = {Scalable Label Propagation for Multi-relational Learning on the Tensor Product of Graphs},
author = {Zhuliu Li and Raphael Petegrosso and Shaden Smith and David Sterling and George Karypis and Rui Kuang},
url = {https://ieeexplore.ieee.org/document/9369895/},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Transactions on Knowledge and Data Engineering},
publisher = {IEEE},
keywords = {Multi-relational learning, Protein-Protein Interaction Network, Tensor Completion},
pubstate = {published},
tppubtype = {article}
}
2019
Li, Zhuliu; Zhang, Wei; Huang, R Stephanie; Kuang, Rui
Learning a Low-rank Tensor of Pharmacogenomic Multi-relations from Biomedical Networks Proceedings
IEEE International Conference on Data Mining 2019.
Abstract | Links | BibTeX | Tags: Multi-relational learning, Tensor Completion
@proceedings{GTCORP2019b,
title = {Learning a Low-rank Tensor of Pharmacogenomic Multi-relations from Biomedical Networks},
author = {Zhuliu Li and Wei Zhang and R Stephanie Huang and Rui Kuang},
url = {http://compbio.cs.umn.edu/08970888.pdf},
year = {2019},
date = {2019-08-31},
organization = {IEEE International Conference on Data Mining},
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 diseasegene, 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.
Source code: https://github.com/kuanglab/GT-COPR},
keywords = {Multi-relational learning, Tensor Completion},
pubstate = {published},
tppubtype = {proceedings}
}
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 diseasegene, 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.
Source code: https://github.com/kuanglab/GT-COPR
Source code: https://github.com/kuanglab/GT-COPR