Publications – Kuang Lab

Isoform Quantification Kernel Method KIR Haplotype Inference Multi-relational learning Phenome-genome Association Protein Remote Homology Detection Protein-Protein Interaction Network scRNA-Seq scRNA-Seq Clustering Sparse Group Learning Spatial Transcriptomics String Kernels Survival Analysis Tensor Completion Transfer Learning

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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

2023

Song, Tianci; Broadbent, Charles; Kuang, Rui

GNTD: Reconstructing Spatial Transcriptomes with Graph-guided Neural Tensor Decomposition Informed by Spatial and Functional Relations Journal Article

In: Nature Communications, vol. 14, no. 8276, 2023.

Links | BibTeX | Tags: Protein-Protein Interaction Network, Spatial Transcriptomics, Tensor Completion

2022

Atkins, Thomas Karl; Song, Tianci; Kuang, Rui

FIST-nD: A tool for n-dimensional spatial transcriptomics data imputation via graph-regularized tensor completio Technical Report

2022.

Links | BibTeX | Tags: Protein-Protein Interaction Network, Spatial Transcriptomics, Tensor Completion

2021

Li, Zhuliu; Song, Tianci; Yong, Jeongsik; Kuang, Rui

Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion Journal Article

In: PLoS computational biology, vol. 17, no. 4, pp. e1008218, 2021.

Links | BibTeX | Tags: Spatial Transcriptomics, Tensor Completion

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

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}

}