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
@article{GNTD,
title = {GNTD: Reconstructing Spatial Transcriptomes with Graph-guided Neural Tensor Decomposition Informed by Spatial and Functional Relations},
author = {Tianci Song and Charles Broadbent and Rui Kuang},
url = {https://www.nature.com/articles/s41467-023-44017-0},
year = {2023},
date = {2023-04-01},
urldate = {2023-04-01},
journal = {Nature Communications},
volume = {14},
number = {8276},
keywords = {Protein-Protein Interaction Network, Spatial Transcriptomics, Tensor Completion},
pubstate = {published},
tppubtype = {article}
}
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
@techreport{FIST_nD,
title = {FIST-nD: A tool for n-dimensional spatial transcriptomics data imputation via graph-regularized tensor completio},
author = {Thomas Karl Atkins and Tianci Song and Rui Kuang
},
url = {https://www.biorxiv.org/content/10.1101/2022.10.12.511928v1.article-metrics},
doi = {10.1101/2022.10.12.511928},
year = {2022},
date = {2022-10-16},
urldate = {2022-10-16},
keywords = {Protein-Protein Interaction Network, Spatial Transcriptomics, Tensor Completion},
pubstate = {published},
tppubtype = {techreport}
}
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}
}
2017
Zhang, Wei; Chien, Jeremy; Yong, Jeongsik; Kuang, Rui
Network-based Machine Learning and Graph Theory Algorithms for Precision Oncology Journal Article
In: NPJ Precision Oncology, no. 25, 2017.
Abstract | Links | BibTeX | Tags: Phenome-genome Association, Protein-Protein Interaction Network
@article{networkreview2017,
title = {Network-based Machine Learning and Graph Theory Algorithms for Precision Oncology},
author = {Wei Zhang and Jeremy Chien and Jeongsik Yong and Rui Kuang},
url = {https://www.nature.com/articles/s41698-017-0029-7},
doi = {doi:10.1038/s41698-017-0029-7},
year = {2017},
date = {2017-08-08},
journal = {NPJ Precision Oncology},
number = {25},
abstract = {Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas (TCGA) and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.},
keywords = {Phenome-genome Association, Protein-Protein Interaction Network},
pubstate = {published},
tppubtype = {article}
}
Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas (TCGA) and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.
2015
Cai, Hong; Lilburn, Timothy G; Hong, Changjin; Gu, Jianying; Kuang, Rui; Wang, Yufeng
Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments Journal Article
In: BMC systems biology, vol. 9, no. 4, pp. 1, 2015.
BibTeX | Tags: Protein-Protein Interaction Network
@article{cai2015predicting,
title = {Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments},
author = {Cai, Hong and Lilburn, Timothy G and Hong, Changjin and Gu, Jianying and Kuang, Rui and Wang, Yufeng},
year = {2015},
date = {2015-01-01},
journal = {BMC systems biology},
volume = {9},
number = {4},
pages = {1},
publisher = {BioMed Central},
keywords = {Protein-Protein Interaction Network},
pubstate = {published},
tppubtype = {article}
}
2008
Hwang, TaeHyun; Tian, Ze; Kuang, Rui; Kocher, Jean-Pierre
Learning on weighted hypergraphs to integrate protein interactions and gene expressions for cancer outcome prediction Proceedings Article
In: 2008 Eighth IEEE International Conference on Data Mining, pp. 293–302, IEEE 2008, ISBN: 978-0-7695-3502-9.
Abstract | Links | BibTeX | Tags: Protein-Protein Interaction Network
@inproceedings{hwang2008learning,
title = {Learning on weighted hypergraphs to integrate protein interactions and gene expressions for cancer outcome prediction},
author = {TaeHyun Hwang and Ze Tian and Rui Kuang and Jean-Pierre Kocher},
url = {http://compbio.cs.umn.edu/wp-content/uploads/2017/10/HyperGene.pdf},
doi = {10.1109/ICDM.2008.37},
isbn = {978-0-7695-3502-9},
year = {2008},
date = {2008-12-15},
booktitle = {2008 Eighth IEEE International Conference on Data Mining},
pages = {293--302},
organization = {IEEE},
abstract = {Abstract:
Building reliable predictive models from multiple complementary genomic data for cancer study is a crucial step towards successful cancer treatment and a full understanding of the underlying biological principles. To tackle this challenging data integration problem, we propose a hypergraph-based learning algorithm called HyperGene to integrate microarray gene expressions and protein-protein interactions for cancer outcome prediction and biomarker identification. HyperGene is a robust two-step iterative method that alternatively finds the optimal outcome prediction and the optimal weighting of the marker genes guided by a protein-protein interaction network. Under the hypothesis that cancer-related genes tend to interact with each other, the HyperGene algorithm uses a protein-protein interaction network as prior knowledge by imposing a consistent weighting of interacting genes. Our experimental results on two large-scale breast cancer gene expression datasets show that HyperGene utilizing a curated protein-protein interaction network achieves significantly improved cancer outcome prediction. Moreover, HyperGene can also retrieve many known cancer genes as highly weighted marker genes.},
keywords = {Protein-Protein Interaction Network},
pubstate = {published},
tppubtype = {inproceedings}
}
Abstract:
Building reliable predictive models from multiple complementary genomic data for cancer study is a crucial step towards successful cancer treatment and a full understanding of the underlying biological principles. To tackle this challenging data integration problem, we propose a hypergraph-based learning algorithm called HyperGene to integrate microarray gene expressions and protein-protein interactions for cancer outcome prediction and biomarker identification. HyperGene is a robust two-step iterative method that alternatively finds the optimal outcome prediction and the optimal weighting of the marker genes guided by a protein-protein interaction network. Under the hypothesis that cancer-related genes tend to interact with each other, the HyperGene algorithm uses a protein-protein interaction network as prior knowledge by imposing a consistent weighting of interacting genes. Our experimental results on two large-scale breast cancer gene expression datasets show that HyperGene utilizing a curated protein-protein interaction network achieves significantly improved cancer outcome prediction. Moreover, HyperGene can also retrieve many known cancer genes as highly weighted marker genes.
Building reliable predictive models from multiple complementary genomic data for cancer study is a crucial step towards successful cancer treatment and a full understanding of the underlying biological principles. To tackle this challenging data integration problem, we propose a hypergraph-based learning algorithm called HyperGene to integrate microarray gene expressions and protein-protein interactions for cancer outcome prediction and biomarker identification. HyperGene is a robust two-step iterative method that alternatively finds the optimal outcome prediction and the optimal weighting of the marker genes guided by a protein-protein interaction network. Under the hypothesis that cancer-related genes tend to interact with each other, the HyperGene algorithm uses a protein-protein interaction network as prior knowledge by imposing a consistent weighting of interacting genes. Our experimental results on two large-scale breast cancer gene expression datasets show that HyperGene utilizing a curated protein-protein interaction network achieves significantly improved cancer outcome prediction. Moreover, HyperGene can also retrieve many known cancer genes as highly weighted marker genes.