Publications – Kuang Lab

@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}

}

Petegrosso, Raphael; Li, Zhuliu; Kuang, Rui

Machine Learning and Statistical Methods for Clustering Single-cell RNA-sequencing Data Journal Article

In: Briefings in Bioinformatics, 2019.

Abstract | Links | BibTeX | Tags: scRNA-Seq, scRNA-Seq Clustering

@article{petegrosso2019scrnaseq,

title = {Machine Learning and Statistical Methods for Clustering Single-cell RNA-sequencing Data},

author = {Raphael Petegrosso and Zhuliu Li and Rui Kuang},

url = {https://doi.org/10.1093/bib/bbz063},

year  = {2019},

date = {2019-06-29},

journal = {Briefings in Bioinformatics},

abstract = {Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell lineages based on the relations among the cells. This article reviews the machine learning and statistical methods for clustering scRNA-seq transcriptomes developed in the past few years. The review focuses on how conventional clustering techniques such as hierarchical clustering, graph-based clustering, mixture models, $k$-means, ensemble learning, neural networks and density-based clustering are modified or customized to tackle the unique challenges in scRNA-seq data analysis, such as the dropout of low-expression genes, low and uneven read coverage of transcripts, highly variable total mRNAs from single cells and ambiguous cell markers in the presence of technical biases and irrelevant confounding biological variations. We review how cell-specific normalization, the imputation of dropouts and dimension reduction methods can be applied with new statistical or optimization strategies to improve the clustering of single cells. We will also introduce those more advanced approaches to cluster scRNA-seq transcriptomes in time series data and multiple cell populations and to detect rare cell types. Several software packages developed to support the cluster analysis of scRNA-seq data are also reviewed and experimentally compared to evaluate their performance and efficiency. Finally, we conclude with useful observations and possible future directions in scRNA-seq data analytics.



AVAILABILITY:

All the source code and data are available at https://github.com/kuanglab/single-cell-review},

keywords = {scRNA-Seq, scRNA-Seq Clustering},

pubstate = {published},

tppubtype = {article}

}

Song, Ying; Song, Tianci; Kuang, Rui

Path segmentation for movement trajectories with irregular sampling frequency using space-time interpolation and density-based spatial clustering Journal Article

In: Transactions in GIS, 23 (3), pp. 558–578, 2019.

Abstract | Links | BibTeX | Tags: Spatial Clustering

Petegrosso, Raphael; Li, Zhuliu; Srour, Molly A.; Saad, Yousef; Zhang, Wei; Kuang, Rui

Scalable Remote Homology Detection and Fold Recognition in Massive Protein Networks Journal Article

In: PROTEINS: Structure, Function, and Bioinformatics, 87 (6), pp. 478-491, 2019.

Abstract | Links | BibTeX | Tags: Protein Remote Homology Detection

@article{scalable2019petegrosso,

title = {Scalable Remote Homology Detection and Fold Recognition in Massive Protein Networks},

author = {Raphael Petegrosso and Zhuliu Li and Molly A. Srour and Yousef Saad and Wei Zhang and Rui Kuang},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.25669},

year  = {2019},

date = {2019-01-31},

journal = {PROTEINS: Structure, Function, and Bioinformatics},

volume = {87},

number = {6},

pages = {478-491},

abstract = {The global connectivities in very large protein similarity networks contain traces of evolution among the proteins for detecting protein remote evolutionary relations or structural similarities. To investigate how well a protein network captures the evolutionary information, a key limitation is the intensive computation of pairwise sequence similarities needed to construct very large protein networks. In this paper, we introduce Label Propagation on Low-rank Kernel Approximation (LP-LOKA) for searching massively large protein networks. LP-LOKA propagates initial protein similarities in a low-rank graph by Nystrom approximation without computing all pairwise similarities. With scalable parallel implementations based on distributed-memory using message-passing interface and Apache-Hadoop/Spark on cloud, LP-LOKA can search protein networks with one million proteins or more. In the experiments on Swiss-Prot/ADDA/CASP data, LP-LOKA significantly improved protein ranking over the widely used HMM-HMM or profile-sequence alignment methods utilizing large protein networks. It was observed that the larger the protein similarity network, the better the performance, especially on relatively small protein superfamilies and folds. The results suggest that computing massively large protein network is necessary to meet the growing need of annotating proteins from newly sequenced species and LP-LOKA is both scalable and accurate for searching massively large protein networks.},

keywords = {Protein Remote Homology Detection},

pubstate = {published},

tppubtype = {article}

}

2018

Hauck, Amy K; Zhou, Tong; Hahn, Wendy S; Petegrosso, Raphael; Kuang, Rui; Chen, Yue; Bernlohr, David A

Obesity-Induced Protein Carbonylation In Murine Adipose Tissue Regulates The DNA Binding Domain Of Nuclear Zinc-Finger Proteins Journal Article

In: Journal of Biological Chemistry, 2018.

Abstract | Links | BibTeX | Tags:

@article{Hauck2018,

title = {Obesity-Induced Protein Carbonylation In Murine Adipose Tissue Regulates The DNA Binding Domain Of Nuclear Zinc-Finger Proteins},

author = {Amy K Hauck and Tong Zhou and Wendy S Hahn and Raphael Petegrosso and Rui Kuang and Yue Chen and David A Bernlohr},

url = {http://www.jbc.org/content/early/2018/07/16/jbc.RA118.003469.abstract},

doi = {doi: 10.1074/jbc.RA118.003469},

year  = {2018},

date = {2018-07-16},

journal = {Journal of Biological Chemistry},

abstract = {In obesity-linked insulin resistance, oxidative stress in adipocytes leads to lipid peroxidation and subsequent carbonylation of proteins by diffusible lipid electrophiles. Reduction in oxidative stress attenuates protein carbonylation and insulin resistance suggesting lipid modification of proteins may play a role in metabolic disease, but the mechanisms remain incompletely understood. Herein we show that in vivo, diet-induced obesity in mice surprisingly results in preferential carbonylation of nuclear proteins by 4-hydroxy-trans 2,3 nonenal (4-HNE) or 4-hydroxy-trans 2,3 hexenal (4-HHE). Proteomic and structural analyses revealed that residues in or around the sites of zinc coordination of zinc finger proteins, such as those containing the C2H2 or MATRIN, RING, C3H1, or N4-type DNA binding domains, are particularly susceptible to carbonylation by lipid aldehydes. These observations strongly suggest that carbonylation functionally disrupts protein secondary structure supported by metal coordination. Analysis of one such target, the nuclear protein estrogen-related receptor gamma (ERR-γ), showed that ERR-γ is modified by 4-HHE in the obese state. In vitro carbonylation decreased the DNA-binding capacity of ERR-γ and correlated with the obesity-linked down regulation of many key genes promoting mitochondrial bioenergetics. Taken together, these findings reveal a novel mechanistic connection between oxidative stress and metabolic dysfunction arising from carbonylation of nuclear zinc-finger proteins such as the transcriptional regulator ERR-γ.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Jae-Woong; Zhang Chang, Wei; Yeh; Yong, Jeongsik#

An integrative model for alternative polyadenylation, IntMAP, delineates mTOR-modulated endoplasmic reticulum stress response Journal Article

In: Nucleic Acids Research, 46 (12), pp. P5996–6008, 2018.

Abstract | BibTeX | Tags: Transcriptome

@article{chang2018,

title = {An integrative model for alternative polyadenylation, IntMAP, delineates mTOR-modulated endoplasmic reticulum stress response},

author = {Chang, Jae-Woong; Zhang, Wei; Yeh, Hsin Sung; Park, Meeyeon; Yao, Chengguo; Shi, Yongsheng; Kuang, Rui# and Yong, Jeongsik#},

year  = {2018},

date = {2018-07-06},

journal = {Nucleic Acids Research},

volume = {46},

number = {12},

pages = {P5996–6008},

abstract = {3'-untranslated regions (UTRs) can vary through the use of alternative polyadenylation sites during pre-mRNA processing. Multiple publically available pipelines combining high profiling technologies and bioinformatics tools have been developed to catalog changes in 3'-UTR lengths. In our recent RNA-seq experiments using cells with hyper-activated mammalian target of rapamycin (mTOR), we found that cellular mTOR activation leads to transcriptome-wide alternative polyadenylation (APA), resulting in the activation of multiple cellular pathways. Here, we developed a novel bioinformatics algorithm, IntMAP, which integrates RNA-Seq and PolyA Site (PAS)-Seq data for a comprehensive characterization of APA events. By applying IntMAP to the datasets from cells with hyper-activated mTOR, we identified novel APA events that could otherwise not be identified by either profiling method alone. Several transcription factors including Cebpg (CCAAT/enhancer binding protein gamma) were among the newly discovered APA transcripts, indicating that diverse transcriptional networks may be regulated by mTOR-coordinated APA. The prevention of APA in Cebpg using the CRISPR/cas9-mediated genome editing tool showed that mTOR-driven 3'-UTR shortening in Cebpg is critical in protecting cells from endoplasmic reticulum (ER) stress. Taken together, we present IntMAP as a new bioinformatics algorithm for APA analysis by which we expand our understanding of the physiological role of mTOR-coordinated APA events to ER stress response. IntMAP toolbox is available at http://compbio.cs.umn.edu/IntMAP/.},

keywords = {Transcriptome},

pubstate = {published},

tppubtype = {article}

}

Kang, Xiaojun; Xu, Gang; Lee, Byungha; Chen, Chen; Zhang, Huanan; Kuang, Rui; Ni, Min

HRB2 and BBX21 interaction modulates Arabidopsis ABI5 locus and stomatal aperture Journal Article

In: Plant, Cell & Environment, (41), pp. 1912-1925, 2018.

Abstract | Links | BibTeX | Tags:

Zhang, Huanan; Lee, Catherine A. A.; Li, Zhuliu; Garbe, John R.; Eide, Cindy R.; Petegrosso, Raphael; Kuang, Rui; Tolar, Jakub

A Multitask Clustering Approach for Single-cell RNA-Seq Analysis in Recessive Dystrophic Epidermolysis Bullosa Journal Article

In: PLOS Computational Biology, 14 (4), 2018.

Abstract | Links | BibTeX | Tags: Multitask learning, scRNA-Seq

@article{multitask_zhang,

title = {A Multitask Clustering Approach for Single-cell RNA-Seq Analysis in Recessive Dystrophic Epidermolysis Bullosa},

author = {Huanan Zhang and Catherine A. A. Lee and Zhuliu Li and John R. Garbe and Cindy R. Eide and Raphael Petegrosso and Rui Kuang and Jakub Tolar},

url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006053},

doi = {https://doi.org/10.1371/journal.pcbi.1006053},

year  = {2018},

date = {2018-04-05},

journal = {PLOS Computational Biology},

volume = {14},

number = {4},

abstract = {Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. 

Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates  and one large-scale Drop-seq dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry.

},

keywords = {Multitask learning, scRNA-Seq},

pubstate = {published},

tppubtype = {article}

}

Xiang, Xiaoyu; Wang, Yuanguo; Zhang, Hongbin; Piao, Jinhua; Muthusamy, Selvaraj; Wang, Lei; Deng, Yibin; Zhang, Wei; Kuang, Rui; Billadeau, Daniel D.; Huang, Shengbing; Lai, Jinping; Urrutia, Raul; Kang, Ningling

Vasodilator-stimulated phosphoprotein promotes liver metastasis of gastrointestinal cancer by activating a β1-integrin-FAK-YAP1/TAZ signaling pathway Journal Article

In: npj Precision Oncology, 2018.

Abstract | Links | BibTeX | Tags:

@article{xiang2018vasodilator,

title = {Vasodilator-stimulated phosphoprotein promotes liver metastasis of gastrointestinal cancer by activating a β1-integrin-FAK-YAP1/TAZ signaling pathway},

author = {Xiaoyu Xiang and Yuanguo Wang and Hongbin Zhang and Jinhua Piao and Selvaraj Muthusamy and Lei Wang and Yibin Deng and Wei Zhang and Rui Kuang and Daniel D. Billadeau and Shengbing Huang and Jinping Lai and Raul Urrutia and Ningling Kang},

url = {https://www.nature.com/articles/s41698-017-0045-7},

doi = {10.1038/s41698-017-0045-7},

year  = {2018},

date = {2018-03-22},

journal = {npj Precision Oncology},

abstract = {Extracellular matrix (ECM)-induced β1-integrin-FAK signaling promotes cell attachment, survival, and migration of cancer cells in a distant organ so as to enable cancer metastasis. However, mechanisms governing activation of the β1-integrin-FAK signaling remain incompletely understood. Here, we report that vasodilator-stimulated phosphoprotein (VASP), an actin binding protein, is required for ECM–mediated β1-integrin-FAK-YAP1/TAZ signaling in gastrointestinal (GI) cancer cells and their liver metastasis. In patient-derived samples, VASP is upregulated in 53 of 63 colorectal cancers and 43 of 53 pancreatic ductal adenocarcinomas and high VASP levels correlate with liver metastasis and reduced patient survival. In a Matrigel-based 3-dimensional (3D) culture model, short hairpin RNA (shRNA)–mediated VASP knockdown in colorectal cancer cells (KM12L4, HCT116, and HT29) and pancreatic cancer cells (L3.6 and MIA PaCa-1) suppresses the growth of 3D cancer spheroids. Mechanistic studies reveal that VASP knockdown suppresses FAK phosphorylation and YAP1/TAZ protein levels, but not Akt or Erk-related pathways and that YAP1/TAZ proteins are enhanced by the β1-integrin-FAK signaling. Additionally, VASP regulates the β1-integrin-FAK-YAP1/TAZ signaling by at least two mechanisms: (1) promoting ECM-mediated β1-integrin activation and (2) regulating YAP1/TAZ dephosphorylation at downstream of RhoA to enhance the stability of YAP1/TAZ proteins. In agreement with these, preclinical studies with two experimental liver metastasis mouse models demonstrate that VASP knockdown suppresses GI cancer liver metastasis, β1-integrin activation, and YAP1/TAZ levels of metastatic cancer cells. Together, our data support VASP as a treatment target for liver metastasis of colorectal and pancreatic cancers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

2017

Zhang, Huanan; Roe, David; Kuang, Rui

Detecting Population-differentiation Copy Number Variants in Human Population Tree by Sparse Group Selection Journal Article

In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16 (2), pp. 538 - 549, 2017.

Abstract | Links | BibTeX | Tags: DNA Copy Number Variation, Sparse Group Learning

@article{Kuang2017,

title = {Detecting Population-differentiation Copy Number Variants in Human Population Tree by Sparse Group Selection},

author = { Huanan Zhang and David Roe and Rui Kuang},

url = {http://ieeexplore.ieee.org/document/8168351/},

year  = {2017},

date = {2017-12-08},

journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},

volume = {16},

number = {2},

pages = {538 - 549},

abstract = {Copy-number variants (CNVs) account for a substantial proportion of human genetic variations. Understanding the CNV diversities across populations is a computational challenge because CNV patterns are often present in several related populations and only occur in a subgroup of individuals within each of the population. This paper introduces a tree-guided sparse group selection algorithm (treeSGS) to detect population-differentiation CNV markers of subgroups across populations organized by a phylogenetic tree of human populations. The treeSGS algorithm detects CNV markers of populations associated with nodes from all levels of the tree such that the evolutionary relations among the populations are incorporated for more accurate detection of population-differentiation CNVs. We applied treeSGS algorithm to study the 1179 samples from the 11 populations in Hapmap3 CNV data. The treeSGS algorithm accurately identifies CNV markers of each population and the collection of populations organized under the branches of the human population tree, validated by consistency among family trios and SNP characterizations of the CNV regions. Further comparison between the detected CNV markers and other population-differentiation CNVs reported in 1000 genome data and other recent studies also shows that treeSGS can significantly improve the current annotations of population-differentiation CNV markers. TreeSGS package is available at http://compbio.cs.umn.edu/treesgs.},

keywords = {DNA Copy Number Variation, Sparse Group Learning},

pubstate = {published},

tppubtype = {article}

}

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, (25), 2017.

Abstract | Links | BibTeX | Tags: Cancer Genomics, Network-based Learning, Phenome-genome Association, Protein-Protein Interaction Network, Semi-supervised Learning

Zhang, Huanan; Cheng, Feng; Xiao, Yuguo; Kang, Xiaojun; Wang, Xiaowu; Kuang, Rui; Ni, Min

Global analysis of canola genes targeted by SHORT HYPOCOTYL UNDER BLUE 1 during endosperm and embryo development Journal Article

In: The Plant Journal, 91 (1), pp. 158-171, 2017.

Abstract | Links | BibTeX | Tags:

@article{huananplant2016b,

title = {Global analysis of canola genes targeted by SHORT HYPOCOTYL UNDER BLUE 1 during endosperm and embryo development},

author = {Huanan Zhang and Feng Cheng and Yuguo Xiao and Xiaojun Kang and Xiaowu Wang and Rui Kuang and Min Ni},

url = {http://onlinelibrary.wiley.com/doi/10.1111/tpj.13542/abstract},

year  = {2017},

date = {2017-07-01},

journal = {The Plant Journal},

volume = {91},

number = {1},

pages = {158-171},

abstract = {Seed development in dicots includes early endosperm proliferation followed by growth of the embryo to replace the endosperm. Endosperm proliferation in dicots not only provides nutrient supplies for subsequent embryo development but also enforces a space limitation, influencing final seed size. Overexpression of Arabidopsis SHORT HYPOCOTYL UNDER BLUE1::uidA (SHB1:uidA) in canola produces large seeds. We performed global analysis of the canola genes that were expressed and influenced by SHB1 during early endosperm proliferation at 8 days after pollination (DAP) and late embryo development at 13 DAP. Overexpression of SHB1 altered the expression of 973 genes at 8 DAP and 1035 genes at 13 DAP. We also surveyed the global SHB1 association sites, and merging of these sites with the RNA sequencing data identified a set of canola genes targeted by SHB1. The 8-DAP list includes positive and negative genes that influence endosperm proliferation and are homologous to Arabidopsis MINI3, IKU2, SHB1, AGL62, FIE and AP2. We revealed a major role for SHB1 in canola endosperm development based on the dynamics of SHB1-altered gene expression, the magnitude of SHB1 chromatin immunoprecipitation enrichment and the over-representation of eight regulatory genes for endosperm development. Our studies focus on an important agronomic trait in a major crop for global agriculture. The datasets on stage-specific and SHB1-induced gene expression and genes targeted by SHB1 also provide a useful resource in the field of endosperm development and seed size engineering. Our practices in an allotetraploid species will impact similar studies in other crop species.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Roe, David; Vierra-Green, Cynthia; Pyo, C-W; Eng, K; Hall, R; Kuang, Rui; Spellman, Stephen; Ranade, S; Geraghty, D E; Maiers, Martin

Revealing complete complex KIR haplotypes phased by long-read sequencing technology Journal Article

In: Genes Immunity, 1-8 , 2017.

Abstract | Links | BibTeX | Tags: KIR Haplotype Inference

@article{KIR2017,

title = {Revealing complete complex KIR haplotypes phased by long-read sequencing technology},

author = {David Roe and Cynthia Vierra-Green and C-W Pyo and K Eng and R Hall and Rui Kuang and Stephen Spellman and S Ranade and D E Geraghty and Martin Maiers},

url = {https://www.nature.com/gene/journal/vaop/ncurrent/full/gene201710a.html},

doi = {10.1038/gene.2017.10},

year  = {2017},

date = {2017-06-01},

journal = {Genes Immunity},

volume = {1-8},

abstract = {The killer cell immunoglobulin-like receptor (KIR) region of human chromosome 19 contains up to 16 genes for natural killer (NK) cell receptors that recognize human leukocyte antigen (HLA)/peptide complexes and other ligands. The KIR proteins fulfill functional roles in infections, pregnancy, autoimmune diseases and transplantation. However, their characterization remains a constant challenge. Not only are the genes highly homologous due to their recent evolution by tandem duplications, but the region is structurally dynamic due to frequent transposon-mediated recombination. A sequencing approach that precisely captures the complexity of KIR haplotypes for functional annotation is desirable. We present a unique approach to haplotype the KIR loci using single-molecule, real-time (SMRT) sequencing. Using this method, we have—for the first time—comprehensively sequenced and phased sixteen KIR haplotypes from eight individuals without imputation. The information revealed four novel haplotype structures, a novel gene-fusion allele, novel and confirmed insertion/deletion events, a homozygous individual, and overall diversity for the structural haplotypes and their alleles. These KIR haplotypes augment our existing knowledge by providing high-quality references, evolutionary informers, and source material for imputation. The haplotype sequences and gene annotations provide alternative loci for the KIR region in the human genome reference GrCh38.p8.},

keywords = {KIR Haplotype Inference},

pubstate = {published},

tppubtype = {article}

}

2016

Petegrosso, Raphael; Park, Sunho; Hwang, Tae Hyun; Kuang, Rui

Transfer Learning across Ontologies for Phenome-Genome Association Prediction Journal Article

In: Bioinformatics, 33 (4), pp. 529-536, 2016.

Abstract | Links | BibTeX | Tags: Phenome-genome Association, Semi-supervised Learning, Transfer Learning

@article{petegrosso2016transfer,

title = {Transfer Learning across Ontologies for Phenome-Genome Association Prediction},

author = {Raphael Petegrosso and Sunho Park and Tae Hyun Hwang and Rui Kuang},

url = {http://bioinformatics.oxfordjournals.org/content/early/2016/10/20/bioinformatics.btw649.abstract},

doi = {10.1093/bioinformatics/btw649},

year  = {2016},

date = {2016-11-23},

journal = {Bioinformatics},

volume = {33},

number = {4},

pages = {529-536},

publisher = {Oxford Univ Press},

abstract = {Motivation: To better predict and analyze gene associations with the collection of phenotypes organized in a phenotype ontology, it is crucial to effectively model the hierarchical structure among the phenotypes in the ontology and leverage the sparse known associations with additional training information. In this paper, we first introduce Dual Label Propagation (DLP) to impose consistent associations with the entire phenotype paths in predicting phenotype-gene associations in Human Phenotype Ontology (HPO). DLP is then used as the base model in a transfer learning framework (tlDLP) to incorporate functional annotations in Gene Ontology (GO). By simultaneously reconstructing GO term-gene associations and HPO phenotype-gene associations for all the genes in a protein-protein interaction network, tlDLP benefits from the enriched training associations indirectly through relation with GO terms. 

 

Results: In the experiments to predict the associations between human genes and phenotypes in HPO based on human protein-protein interaction network, both DLP and tlDLP improved the prediction of gene associations with phenotype paths in HPO in cross-validation and the prediction of the most recent associations added after the snapshot of the training data. Moreover, the transfer learning through GO term-gene associations significantly improved association predictions for the phenotypes with no more specific known associations by a large margin. Examples are also shown to demonstrate how phenotype paths in phenotype ontology and transfer learning with gene ontology can improve the predictions. 

 

Availability: Source code is available at http://localhost/~raphaelpetegrosso/wpcb/ontophenome.},

keywords = {Phenome-genome Association, Semi-supervised Learning, Transfer Learning},

pubstate = {published},

tppubtype = {article}

}

Vierra-Green, Cynthia; Roe, David; Jayaraman, Jyothi; Trowsdale, John; Traherne, James; Kuang, Rui; Spellman, Stephen; Maiers, Martin

Estimating KIR Haplotype Frequencies on a Cohort of 10,000 Individuals: A Comprehensive Study on Population Variations, Typing Resolutions, and Reference Haplotypes Journal Article

In: PloS one, 11 (10), pp. e0163973, 2016.

Abstract | Links | BibTeX | Tags: KIR Haplotype Inference

@article{vierra2016estimating,

title = {Estimating KIR Haplotype Frequencies on a Cohort of 10,000 Individuals: A Comprehensive Study on Population Variations, Typing Resolutions, and Reference Haplotypes},

author = {Cynthia Vierra-Green and David Roe and Jyothi Jayaraman and John Trowsdale and James Traherne and Rui Kuang and Stephen Spellman and Martin Maiers},

url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0163973},

doi = {10.1371/journal.pone.0163973},

year  = {2016},

date = {2016-10-10},

journal = {PloS one},

volume = {11},

number = {10},

pages = {e0163973},

publisher = {Public Library of Science},

abstract = {The killer cell immunoglobulin-like receptors (KIR) mediate human natural killer (NK) cell cytotoxicity via activating or inhibiting signals. Although informative and functional haplotype patterns have been reported, most genotyping has been performed at resolutions that are structurally ambiguous. In order to leverage structural information given low-resolution genotypes, we performed experiments to quantify the effects of population variations, reference haplotypes, and genotyping resolutions on population-level haplotype frequency estimations as well as predictions of individual haplotypes. We genotyped 10,157 unrelated individuals in 5 populations (518 African American[AFA], 258 Asian or Pacific Islander[API], 8,245 European[EUR], 1,073 Hispanic[HIS], and 63 Native American[NAM]) for KIR gene presence/absence (PA), and additionally half of the AFA samples for KIR gene copy number variation (CNV). A custom EM algorithm was used to estimate haplotype frequencies for each population by interpretation in the context of three sets of reference haplotypes. The algorithm also assigns each individual the haplotype pairs of maximum likelihood. Generally, our haplotype frequency estimates agree with similar previous publications to within <5% difference for all haplotypes. The exception is that estimates for NAM from the U.S. showed higher frequency association of cB02 with tA01 (+14%) instead of tB01 (-8.5%) compared to a previous study of NAM from south of the U.S. The higher-resolution CNV genotyping on the AFA samples allowed unambiguous haplotype-pair assignments for the majority of individuals, resulting in a 22% higher median typing resolution score (TRS), which measures likelihood of self-match in the context of population-specific haplo- and geno-types. The use of TRS to quantify reduced ambiguity with CNV data clearly revealed the few individuals with ambiguous genotypes as outliers. It is observed that typing resolution and reference haplotype set influence haplotype frequency estimates. For example, PA resolution may be used with reference haplotype sets up to the point where certain haplotypes are gene-content subsets of others. At that point, CNV must be used for all genes.},

keywords = {KIR Haplotype Inference},

pubstate = {published},

tppubtype = {article}

}

The killer cell immunoglobulin-like receptors (KIR) mediate human natural killer (NK) cell cytotoxicity via activating or inhibiting signals. Although informative and functional haplotype patterns have been reported, most genotyping has been performed at resolutions that are structurally ambiguous. In order to leverage structural information given low-resolution genotypes, we performed experiments to quantify the effects of population variations, reference haplotypes, and genotyping resolutions on population-level haplotype frequency estimations as well as predictions of individual haplotypes. We genotyped 10,157 unrelated individuals in 5 populations (518 African American[AFA], 258 Asian or Pacific Islander[API], 8,245 European[EUR], 1,073 Hispanic[HIS], and 63 Native American[NAM]) for KIR gene presence/absence (PA), and additionally half of the AFA samples for KIR gene copy number variation (CNV). A custom EM algorithm was used to estimate haplotype frequencies for each population by interpretation in the context of three sets of reference haplotypes. The algorithm also assigns each individual the haplotype pairs of maximum likelihood. Generally, our haplotype frequency estimates agree with similar previous publications to within <5% difference for all haplotypes. The exception is that estimates for NAM from the U.S. showed higher frequency association of cB02 with tA01 (+14%) instead of tB01 (-8.5%) compared to a previous study of NAM from south of the U.S. The higher-resolution CNV genotyping on the AFA samples allowed unambiguous haplotype-pair assignments for the majority of individuals, resulting in a 22% higher median typing resolution score (TRS), which measures likelihood of self-match in the context of population-specific haplo- and geno-types. The use of TRS to quantify reduced ambiguity with CNV data clearly revealed the few individuals with ambiguous genotypes as outliers. It is observed that typing resolution and reference haplotype set influence haplotype frequency estimates. For example, PA resolution may be used with reference haplotype sets up to the point where certain haplotypes are gene-content subsets of others. At that point, CNV must be used for all genes.

Liang, Lining; Sun, Hao; Zhang, Wei; Zhang, Mengdan; Yang, Xiao; Kuang, Rui; Zheng, Hui

Meta-Analysis of EMT Datasets Reveals Different Types of EMT. Journal Article

In: PloS one, 11 (6), pp. e0156839–e0156839, 2016.

Abstract | Links | BibTeX | Tags: Gene Expression, Transcriptome

2015

Zhang, Wei; Chang, Jae-Woong; Lin, Lilong; Minn, Kay; Wu, Baolin; Chien, Jeremy; Yong, Jeongsik; Zheng, Hui; Kuang, Rui

Network-based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis Journal Article

In: PLoS Computational Biology, e1004465 , 2015.

Abstract | Links | BibTeX | Tags: Cancer Genomics, Isoform Quantification, Network-based Learning

Chang, Jae-Woong; Zhang, Wei; Yeh, Hsin-Sung; de Jong, Ebbing P; Jun, Semo; Kim, Kwan-Hyun; Bae, Sun S; Beckman, Kenneth; Hwang, Tae Hyun; Kim, Kye-Seong; others,

mRNA 3'-UTR shortening is a molecular signature of mTORC1 activation Journal Article

In: Nature communications, 6 , 2015.

Abstract | Links | BibTeX | Tags:

Xie, MaoQiang; Xu, YingJie; Zhang, YaoGong; Hwang, TaeHyun; Kuang, Rui

Network-based Phenome-Genome Association Prediction by Bi-Random Walk Journal Article

In: PloS one, 10 (5), pp. e0125138, 2015.

Abstract | Links | BibTeX | Tags: Phenome-genome Association, Semi-supervised Learning

Chien, Jeremy; Sicotte, Hugues; Fan, Jian-Bing; Humphray, Sean; Cunningham, Julie M; Kalli, Kimberly R; Oberg, Ann L; Hart, Steven N; Li, Ying; Davila, Jaime I; others,

TP53 mutations, tetraploidy and homologous recombination repair defects in early stage high-grade serous ovarian cancer Journal Article

In: Nucleic acids research, pp. gkv111, 2015.

Abstract | Links | BibTeX | Tags: Cancer Genomics

Johnson, Nicholas; Zhang, Huanan; Fang, Gang; Kumar, Vipin; Kuang, Rui

SubPatCNV: approximate subspace pattern mining for mapping copy-number variations Journal Article

In: BMC bioinformatics, 16 (1), pp. 1, 2015, ISSN: 1471-2105.

Abstract | Links | BibTeX | Tags: Cancer Genomics, DNA Copy Number Variation

@article{johnson2015subpatcnv,

title = {SubPatCNV: approximate subspace pattern mining for mapping copy-number variations},

author = {Nicholas Johnson and Huanan Zhang and Gang Fang and Vipin Kumar and Rui Kuang},

url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-014-0426-7},

doi = {10.1186/s12859-014-0426-7},

issn = {1471-2105},

year  = {2015},

date = {2015-01-16},

journal = {BMC bioinformatics},

volume = {16},

number = {1},

pages = {1},

publisher = {BioMed Central},

abstract = {Background 

Many DNA copy-number variations (CNVs) are known to lead to phenotypic variations and pathogenesis. While CNVs are often only common in a small number of samples in the studied population or patient cohort, previous work has not focused on customized identification of CNV regions that only exhibit in subsets of samples with advanced data mining techniques to reliably answer questions such as “Which are all the chromosomal fragments showing nearly identical deletions or insertions in more than 30% of the individuals?”. 

 

Results 

We introduce a tool for mining CNV subspace patterns, namely SubPatCNV, which is capable of identifying all aberrant CNV regions specific to arbitrary sample subsets larger than a support threshold. By design, SubPatCNV is the implementation of a variation of approximate association pattern mining algorithm under a spatial constraint on the positional CNV probe features. In benchmark test, SubPatCNV was applied to identify population specific germline CNVs from four populations of HapMap samples. In experiments on the TCGA ovarian cancer dataset, SubPatCNV discovered many large aberrant CNV events in patient subgroups, and reported regions enriched with cancer relevant genes. In both HapMap data and TCGA data, it was observed that SubPatCNV employs approximate pattern mining to more effectively identify CNV subspace patterns that are consistent within a subgroup from high-density array data. 

 

Conclusions 

SubPatCNV available through http://sourceforge.net/projects/subpatcnv/is a unique scalable open-source software tool that provides the flexibility of identifying CNV regions specific to sample subgroups of different sizes from high-density CNV array data.},

keywords = {Cancer Genomics, DNA Copy Number Variation},

pubstate = {published},

tppubtype = {article}

}

Sharma, Ankit; Kuang, Rui; Srivastava, Jaideep; Feng, Xiaodong; Singhal, Kartik

Predicting small group accretion in social networks: A topology based incremental approach Inproceedings

In: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 408–415, IEEE 2015, ISBN: 978-1-4503-3854-7/15/08.

Abstract | Links | BibTeX | Tags: Semi-supervised Learning

@inproceedings{sharma2015predicting,

title = {Predicting small group accretion in social networks: A topology based incremental approach},

author = {Ankit Sharma and Rui Kuang and Jaideep Srivastava and Xiaodong Feng and Kartik Singhal},

url = {http://delivery.acm.org/10.1145/2810000/2808914/p408_sharma.pdf},

doi = {http://dx.doi.org/10.1145/2808797.2808914},

isbn = {978-1-4503-3854-7/15/08},

year  = {2015},

date = {2015-01-01},

booktitle = {2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},

pages = {408--415},

organization = {IEEE},

abstract = {Small Group evolution has been of central importance in social sciences and also in the industry for understanding dynamics of team formation. While most of research works studying groups deal at a macro level with evolution of arbitrary size communities, in this paper we restrict ourselves to studying evolution of small group (size ≤ 20) which is governed by contrasting sociological phenomenon. Given a previous history of group collaboration between a set of actors, we address the problem of predicting likely future group collaborations. Unfortunately, predicting groups requires choosing from (n choose r) possibilities (where r is group size and n is total number of actors), which becomes computationally intractable as group size increases. However, our statistical analysis of a real world dataset has shown that two processes: an external actor joining an existing group (incremental accretion (IA)) or collaborating with a subset of actors of an exiting group (subgroup accretion (SA)), are largely responsible for future group formation. This helps to drastically reduce the (n choose r) possibilities. We therefore, model the attachment of a group for different actors outside this group. In this paper, we have built three topology based prediction models to study these phenomena. The performance of these models is evaluated using extensive experiments over DBLP dataset. Our prediction results shows that the proposed models are significantly useful for future group predictions both for IA and SA.},

keywords = {Semi-supervised Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

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, 9 (4), pp. 1, 2015.

BibTeX | Tags: Network Alignment, Protein-Protein Interaction Network

2013

Zhang, Huanan; Tian, Ze; Kuang, Rui

Transfer learning across cancers on DNA copy number variation analysis Inproceedings

In: 2013 IEEE 13th International Conference on Data Mining, pp. 1283–1288, IEEE IEEE, 2013, ISBN: 978-0-7695-5108-1.

Abstract | Links | BibTeX | Tags: Cancer Genomics, DNA Copy Number Variation, Transfer Learning

@inproceedings{zhang2013transfer,

title = {Transfer learning across cancers on DNA copy number variation analysis},

author = {Huanan Zhang and Ze Tian and Rui Kuang},

url = {http://compbio.cs.umn.edu/wp-content/uploads/2017/10/TLFL-10Page.pdf},

doi = {10.1109/ICDM.2013.58},

isbn = {978-0-7695-5108-1},

year  = {2013},

date = {2013-12-07},

booktitle = {2013 IEEE 13th International Conference on Data Mining},

pages = {1283--1288},

publisher = {IEEE},

organization = {IEEE},

abstract = {Abstract: 

DNA copy number variations (CNVs) are prevalent in all types of tumors. It is still a challenge to study how CNVs play a role in driving tumorgenic mechanisms that are either universal or specific in different cancer types. To address the problem, we introduce a transfer learning framework to discover common CNVs shared across different tumor types as well as CNVs specific to each tumor type from genome-wide CNV data measured by array CGH and SNP genotyping array. The proposed model, namely Transfer Learning with Fused LASSO (TLFL), detects latent CNV components from multiple CNV datasets of different tumor types to distinguish the CNVs that are common across the datasets and those that are specific in each dataset. Both the common and type-specific CNVs are detected as latent components in matrix factorization coupled with fused LASSO on adjacent CNV probe features. TLFL considers the common latent components underlying the multiple datasets to transfer knowledge across different tumor types. In simulations and experiments on real cancer CNV datasets, TLFL detected better latent components that can be used as features to improve classification of patient samples in each individual dataset compared with the model without the knowledge transfer. In cross-dataset analysis on bladder cancer and cross-domain analysis on breast cancer and ovarian cancer, TLFL also learned latent CNV components that are both predictive of tumor stages and correlate with known cancer genes.},

keywords = {Cancer Genomics, DNA Copy Number Variation, Transfer Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Cai, Hong; Hong, Changjin; Lilburn, Timothy G; Rodriguez, Armando L; Chen, Sheng; Gu, Jianying; Kuang, Rui; Wang, Yufeng

A novel subnetwork alignment approach predicts new components of the cell cycle regulatory apparatus in Plasmodium falciparum Journal Article

In: BMC bioinformatics, 14 (12), pp. 1, 2013, ISSN: 1471-2105.

Abstract | Links | BibTeX | Tags: Network Alignment

@article{cai2013novel,

title = {A novel subnetwork alignment approach predicts new components of the cell cycle regulatory apparatus in Plasmodium falciparum},

author = {Hong Cai and Changjin Hong and Timothy G Lilburn and Armando L Rodriguez and Sheng Chen and Jianying Gu and Rui Kuang and Yufeng Wang},

url = {http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-S12-S2},

doi = {10.1186/1471-2105-14-S12-S2},

issn = {1471-2105},

year  = {2013},

date = {2013-09-24},

journal = {BMC bioinformatics},

volume = {14},

number = {12},

pages = {1},

publisher = {BioMed Central},

abstract = {Background 

According to the World Health organization, half the world's population is at risk of contracting malaria. They estimated that in 2010 there were 219 million cases of malaria, resulting in 660,000 deaths and an enormous economic burden on the countries where malaria is endemic. The adoption of various high-throughput genomics-based techniques by malaria researchers has meant that new avenues to the study of this disease are being explored and new targets for controlling the disease are being developed. Here, we apply a novel neighborhood subnetwork alignment approach to identify the interacting elements that help regulate the cell cycle of the malaria parasite Plasmodium falciparum. 

 

Results 

Our novel subnetwork alignment approach was used to compare networks in Escherichia coli and P. falciparum. Some 574 P. falciparum proteins were revealed as functional orthologs of known cell cycle proteins in E. coli. Over one third of these predicted functional orthologs were annotated as "conserved Plasmodium proteins" or "putative uncharacterized proteins" of unknown function. The predicted functionalities included cyclins, kinases, surface antigens, transcriptional regulators and various functions related to DNA replication, repair and cell division. 

 

Conclusions 

The results of our analysis demonstrate the power of our subnetwork alignment approach to assign functionality to previously unannotated proteins. Here, the focus was on proteins involved in cell cycle regulation. These proteins are involved in the control of diverse aspects of the parasite lifecycle and of important aspects of pathogenesis.},

keywords = {Network Alignment},

pubstate = {published},

tppubtype = {article}

}

Chien, Jeremy; Kuang, Rui; Landen, Charles; Shridhar, Viji

Platinum-sensitive recurrence in ovarian cancer: the role of tumor microenvironment Journal Article

In: Frontiers in oncology, 3 , pp. 251, 2013.

Abstract | Links | BibTeX | Tags: Cancer Genomics

Hwang, TaeHyun; Atluri, Gowtham; Kuang, Rui; Kumar, Vipin; Starr, Timothy; Silverstein, Kevin AT; Haverty, Peter M; Zhang, Zemin; Liu, Jinfeng

Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers Journal Article

In: BMC genomics, 14 (1), pp. 440, 2013.

Abstract | Links | BibTeX | Tags: Cancer Genomics, DNA Copy Number Variation, Network-based Learning

Zhang, Wei; Ota, Takayo; Shridhar, Viji; Chien, Jeremy; Wu, Baolin; Kuang, Rui

Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment Journal Article

In: PLoS Comput Biol, 9 (3), pp. e1002975, 2013.

Abstract | Links | BibTeX | Tags: Cancer Genomics, Network-based Learning, Survival Analysis, Transcriptome

@article{zhang2013network,

title = {Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment},

author = {Wei Zhang and Takayo Ota and Viji Shridhar and Jeremy Chien and Baolin Wu and Rui Kuang},

url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002975},

doi = {10.1371/journal.pcbi.1002975},

year  = {2013},

date = {2013-03-21},

journal = {PLoS Comput Biol},

volume = {9},

number = {3},

pages = {e1002975},

publisher = {Public Library of Science},

abstract = {Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L1-norm or L2-norm. This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://localhost/~raphaelpetegrosso/wpcb/Net-Cox/.},

keywords = {Cancer Genomics, Network-based Learning, Survival Analysis, Transcriptome},

pubstate = {published},

tppubtype = {article}

}

Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L1-norm or L2-norm. This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://localhost/~raphaelpetegrosso/wpcb/Net-Cox/.

2012

Tian, Ze; Zhang, Huanan; Kuang, Rui

Sparse group selection on fused lasso components for identifying group-specific DNA copy number variations Inproceedings

In: 2012 IEEE 12th International Conference on Data Mining, pp. 665–674, IEEE IEEE, 2012, ISBN: 978-1-4673-4649-8.

Abstract | Links | BibTeX | Tags: DNA Copy Number Variation, Sparse Group Learning

@inproceedings{tian2012sparse,

title = {Sparse group selection on fused lasso components for identifying group-specific DNA copy number variations},

author = {Ze Tian and Huanan Zhang and Rui Kuang},

url = {http://compbio.cs.umn.edu/wp-content/uploads/2017/10/SGS-FL.pdf},

doi = {10.1109/ICDM.2012.35},

isbn = {978-1-4673-4649-8},

year  = {2012},

date = {2012-12-10},

booktitle = {2012 IEEE 12th International Conference on Data Mining},

pages = {665--674},

publisher = {IEEE},

organization = {IEEE},

abstract = {Detecting DNA copy number variations (CNVs) from arrayCGH or genotyping-array data to correlate with cancer outcomes is crucial for understanding the molecular mechanisms underlying cancer. Previous methods either focus on detecting CNVs in each individual patient sample or common CNVs across all the patient samples. These methods ignore the discrepancies introduced by the heterogeneity in the patient samples, which implies that common CNVs might only be shared within some groups of samples instead of all samples. In this paper, we propose a latent feature model that couples sparse sample group selection with fused lasso on CNV components to identify group-specific CNVs. Assuming a given group structure on patient samples by clinical information, sparse group selection on fused lasso (SGS-FL) identifies the optimal latent CNV components, each of which is specific to the samples in one or several groups. The group selection for each CNV component is determined dynamically by an adaptive algorithm to achieve a desired sparsity. Simulation results show that SGS-FL can more accurately identify the latent CNV components when there is a reliable underlying group structure in the samples. In the experiments on arrayCGH breast cancer and bladder cancer datasets, SGS-FL detected CNV regions that are more relevant to cancer, and provided latent feature weights that can be used for better sample classification.},

keywords = {DNA Copy Number Variation, Sparse Group Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Zhang, Wei; Johnson, Nicholas; Wu, Baolin; Kuang, Rui

Signed network propagation for detecting differential gene expressions and DNA copy number variations Inproceedings

In: Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine, pp. 337–344, ACM 2012.

Abstract | Links | BibTeX | Tags: DNA Copy Number Variation, Gene Expression, Semi-supervised Learning

@inproceedings{zhang2012signedb,

title = {Signed network propagation for detecting differential gene expressions and DNA copy number variations},

author = {Wei Zhang and Nicholas Johnson and Baolin Wu and Rui Kuang},

url = {http://compbio.cs.umn.edu/wp-content/uploads/2017/10/SignedNP-1.pdf},

year  = {2012},

date = {2012-10-07},

booktitle = {Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine},

pages = {337--344},

organization = {ACM},

abstract = {Network propagation algorithms have proved useful for the analysis of high-dimensional genomic data. One limitation is that the current formulation only allows network propagation on positively weighted graphs. In this paper, we explore two signed network propagation algorithms and general optimization frameworks for detecting differential gene expressions and DNA copy number variations (CNV). The proposed algorithms consider both positive and negative relations in graphs to model gene up/down-regulation or amplification/deletion CNV events. The first algorithm (Signed-NP) integrates gene co-expressions and differential expressions for consistent and robust gene selection from microarray datasets by propagation on gene correlation graphs. The second algorithm (Signed-NPBi) identifies gene or CNV markers by propagation on sample-feature bipartite graphs to capture bi-clusters between samples and genomic features. Large scale experiments on several microarray gene expression datasets and CNV datasets validate that Signed-NP and Signed-NPBi perform better classification of gene expression and CNV data than standard network propagation. The experiments also demonstrate that Signed-NP is capable of selecting genes that are more biologically interpretable and consistent across multiple datasets, and Signed-NPBi can detect hidden CNV patterns in bi-clusters by smoothing on correlations between adjacent probes.},

keywords = {DNA Copy Number Variation, Gene Expression, Semi-supervised Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Hwang, TaeHyun; Atluri, Gowtham; Xie, MaoQiang; Dey, Sanjoy; Hong, Changjin; Kumar, Vipin; Kuang, Rui

Co-clustering phenome--genome for phenotype classification and disease gene discovery Journal Article

In: Nucleic acids research, 40 (19), pp. e146–e146, 2012.

Abstract | Links | BibTeX | Tags: Network-based Learning, Phenome-genome Association, Semi-supervised Learning

@article{hwang2012co,

title = {Co-clustering phenome--genome for phenotype classification and disease gene discovery},

author = {TaeHyun Hwang and Gowtham Atluri and MaoQiang Xie and Sanjoy Dey and Changjin Hong and Vipin Kumar and Rui Kuang},

url = {http://nar.oxfordjournals.org/content/40/19/e146.short},

doi = {10.1093/nar/gks615},

year  = {2012},

date = {2012-06-26},

journal = {Nucleic acids research},

volume = {40},

number = {19},

pages = {e146--e146},

publisher = {Oxford Univ Press},

abstract = {Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.},

keywords = {Network-based Learning, Phenome-genome Association, Semi-supervised Learning},

pubstate = {published},

tppubtype = {article}

}

Xie, Maoqiang; Hwang, Taehyun; Kuang, Rui

Prioritizing disease genes by bi-random walk Inproceedings

In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 292–303, Springer 2012.

Abstract | Links | BibTeX | Tags: Phenome-genome Association, Semi-supervised Learning

Tian, Ze; Kuang, Rui

Global Linear Neighborhoods for Efficient Label Propagation. Inproceedings

In: SDM, pp. 863–872, SIAM 2012, ISBN: 978-1-61197-232-0.

Abstract | Links | BibTeX | Tags: Semi-supervised Learning

2011

Cai, Hong; Kuang, Rui; Gu, Jianying; Wang, Yufeng

Proteases in malaria parasites-a phylogenomic perspective Journal Article

In: Current genomics, 12 (6), pp. 417–427, 2011.

Abstract | Links | BibTeX | Tags:

Hwang, TaeHyun; Zhang, Wei; Xie, Maoqiang; Liu, Jinfeng; Kuang, Rui

Inferring disease and gene set associations with rank coherence in networks Journal Article

In: Bioinformatics, 27 (19), pp. 2692–2699, 2011.

Abstract | Links | BibTeX | Tags: Phenome-genome Association, Semi-supervised Learning

@article{hwang2011inferring,

title = {Inferring disease and gene set associations with rank coherence in networks},

author = {TaeHyun Hwang and Wei Zhang and Maoqiang Xie and Jinfeng Liu and Rui Kuang},

url = {http://bioinformatics.oxfordjournals.org/content/27/19/2692},

doi = {10.1093/bioinformatics/btr463},

year  = {2011},

date = {2011-08-02},

journal = {Bioinformatics},

volume = {27},

number = {19},

pages = {2692--2699},

publisher = {Oxford Univ Press},

abstract = {Motivation: To validate the candidate disease genes identified from high-throughput genomic studies, a necessary step is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotypes and the gene sets with a short list of poorly annotated genes, because the existing annotations of disease-causative genes are incomplete. This article introduces a network-based computational approach called rcNet to discover the associations between gene sets and disease phenotypes. A learning framework is proposed to maximize the coherence between the predicted phenotype–gene set relations and the known disease phenotype-gene associations. An efficient algorithm coupling ridge regression with label propagation and two variants are designed to find the optimal solution to the objective functions of the learning framework. 

 

Results: We evaluated the rcNet algorithms with leave-one-out cross-validation on Online Mendelian Inheritance in Man (OMIM) data and an independent test set of recently discovered disease–gene associations. In the experiments, the rcNet algorithms achieved best overall rankings compared with the baselines. To further validate the reproducibility of the performance, we applied the algorithms to identify the target diseases of novel candidate disease genes obtained from recent studies of Genome-Wide Association Study (GWAS), DNA copy number variation analysis and gene expression profiling. The algorithms ranked the target disease of the candidate genes at the top of the rank list in many cases across all the three case studies.},

keywords = {Phenome-genome Association, Semi-supervised Learning},

pubstate = {published},

tppubtype = {article}

}

Jacko, Julie A; Johnson, Layne M; Adam, Terrence J; Ali, Adel L; Chan, Daniel; Kuang, Rui; Nelson, Andrew F; Watters, Amy; Westra, Bonnie; Fauchald, Sally; others,

Community engagement and outreach as curricular and pedagogical tools for consortial delivery of health informatics curricula Journal Article

In: International Journal of Information and Operations Management Education, 4 (3-4), pp. 284–308, 2011, ISSN: 1744-2311.

Abstract | Links | BibTeX | Tags:

2010

Zhang, Wei; Hwang, Baryun; Wu, Baolin; Kuang, Rui

Network propagation models for gene selection Inproceedings

In: 2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), IEEE, 2010, ISBN: 978-1-61284-791-7.

Abstract | Links | BibTeX | Tags: Cancer Genomics, Gene Expression, Semi-supervised Learning

Tian, Ze; Kuang, Rui

Integrative classification and analysis of multiple arrayCGH datasets with probe alignment Journal Article

In: Bioinformatics, 26 (18), pp. 2313–2320, 2010, ISSN: 1460-2059.

Abstract | Links | BibTeX | Tags: DNA Copy Number Variation, Kernel Method

@article{tian2010integrative,

title = {Integrative classification and analysis of multiple arrayCGH datasets with probe alignment},

author = {Ze Tian and Rui Kuang},

url = {http://bioinformatics.oxfordjournals.org/content/26/18/2313.short},

doi = {10.1093/bioinformatics/btq428},

issn = {1460-2059},

year  = {2010},

date = {2010-07-18},

journal = {Bioinformatics},

volume = {26},

number = {18},

pages = {2313--2320},

publisher = {Oxford Univ Press},

abstract = {Motivation: Array comparative genomic hybridization (arrayCGH) is widely used to measure DNA copy numbers in cancer research. ArrayCGH data report log-ratio intensities of thousands of probes sampled along the chromosomes. Typically, the choices of the locations and the lengths of the probes vary in different experiments. This discrepancy in choosing probes poses a challenge in integrated classification or analysis across multiple arrayCGH datasets. We propose an alignment-based framework to integrate arrayCGH samples generated from different probe sets. The alignment framework seeks an optimal alignment between the probe series of one arrayCGH sample and the probe series of another sample, intended to find the maximum possible overlap of DNA copy number variations between the two measured chromosomes. An alignment kernel is introduced for integrative patient sample classification and a multiple alignment algorithm is also introduced for identifying common regions with copy number aberrations. 

 

Results: The probe alignment kernel and the MPA algorithm were experimented to integrate three bladder cancer datasets as well as artificial datasets. In the experiments, by integrating arrayCGH samples from multiple datasets, the probe alignment kernel used with support vector machines significantly improved patient sample classification accuracy over other baseline kernels. The experiments also demonstrated that the multiple probe alignment (MPA) algorithm can find common DNA aberrations that cannot be identified with the standard interpolation method. Furthermore, the MPA algorithm also identified many known bladder cancer DNA aberrations containing four known bladder cancer genes, three of which cannot be detected by interpolation.},

keywords = {DNA Copy Number Variation, Kernel Method},

pubstate = {published},

tppubtype = {article}

}

Hwang, TaeHyun; Kuang, Rui

A Heterogeneous Label Propagation Algorithm for Disease Gene Discovery Inproceedings

In: Society for Industrial and Applied Mathematics. Proceedings of the SIAM International Conference on Data Mining, pp. 583, Society for Industrial and Applied Mathematics 2010.

Abstract | Links | BibTeX | Tags: Phenome-genome Association, Semi-supervised Learning

@inproceedings{hwang2010heterogeneous,

title = {A Heterogeneous Label Propagation Algorithm for Disease Gene Discovery},

author = {TaeHyun Hwang and Rui Kuang},

url = {http://compbio.cs.umn.edu/wp-content/uploads/2017/10/SDM2010.pdf},

doi = {10.1137/1.9781611972801.51},

year  = {2010},

date = {2010-04-29},

booktitle = {Society for Industrial and Applied Mathematics. Proceedings of the SIAM International Conference on Data Mining},

pages = {583},

organization = {Society for Industrial and Applied Mathematics},

abstract = {Label propagation is an effective and efficient technique to utilize local and global features in a network for semi-supervised learning. In the literature, one challenge is how to propagate information in heterogeneous networks comprising several subnetworks, each of which has its own cluster structures that need to be explored independently. In this paper, we introduce an intutitive algorithm MINProp (Mutual Interaction-based Network Propagation) and a simple regularization framework for propagating information between subnetworks in a heterogeneous network. MINProp sequentially performs label propagation on each individual subnetwork with the current label information derived from the other subnetworks and repeats this step until convergence to the global optimal solution to the convex objective function of the regularization framework. The independent label propagation on each subnetwork explores the cluster structure in the subnetwork. The label information from the other subnetworks is used to capture mutual interactions (bicluster structures) between the vertices in each pair of the subnetworks. MINProp algorithm is applied to disease gene discovery from a heterogeneus network of disease phenotypes and genes. In the experiments, MINProp significantly output-performed the original label propagation algorithm on a single network and the state-of-the-art methods for discovering disease genes. The results also suggest that MINProp is more effective in utilizing the modular structures in a heterogenous network. Finally, MINProp discovered new disease-gene associations that are only reported recently.},

keywords = {Phenome-genome Association, Semi-supervised Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Fang, Gang; Kuang, Rui; Pandey, Gaurav; Steinbach, Michael; Myers, Chad L; Kumar, Vipin

Subspace differential coexpression analysis: problem definition and a general approach. Inproceedings

In: Pacific symposium on biocomputing, pp. 145–56, 2010.

Abstract | Links | BibTeX | Tags: Gene Expression, Transcriptome

@inproceedings{fang2010subspace,

title = {Subspace differential coexpression analysis: problem definition and a general approach.},

author = {Gang Fang and Rui Kuang and Gaurav Pandey and Michael Steinbach and Chad L Myers and Vipin Kumar},

url = {http://compbio.cs.umn.edu/wp-content/uploads/2017/10/9789814295291_0017.pdf},

doi = {10.1142/9789814295291_0017},

year  = {2010},

date = {2010-01-04},

booktitle = {Pacific symposium on biocomputing},

volume = {15},

pages = {145--56},

abstract = {In this paper, we study methods to identify differential coexpression patterns in case-control gene expression data. A differential coexpression pattern consists of a set of genes that have substantially different levels of coherence of their expression profiles across the two sample-classes, i.e., highly coherent in one class, but not in the other. Biologically, a differential coexpression patterns may indicate the disruption of a regulatory mechanism possibly caused by disregulation of pathways or mutations of transcription factors. A common feature of all the existing approaches for differential coexpression analysis is that the coexpression of a set of genes is measured on all the samples in each of the two classes, i.e., over the full-space of samples. Hence, these approaches may miss patterns that only cover a subset of samples in each class, i.e., subspace patterns, due to the heterogeneity of the subject population and disease causes. In this paper, we extend differential coexpression analysis by defining a subspace differential coexpression pattern, i.e., a set of genes that are coexpressed in a relatively large percent of samples in one class, but in a much smaller percent of samples in the other class. We propose a general approach based upon association analysis framework that allows exhaustive yet efficient discovery of subspace differential coexpression patterns. This approach can be used to adapt a family of biclustering algorithms to obtain their corresponding differential versions that can directly discover differential coexpression patterns. Using a recently developed biclustering algorithm as illustration, we perform experiments on cancer datasets which demonstrates the existence of subspace differential coexpression patterns. Permutation tests demonstrate the statistical significance for a large number of discovered subspace patterns, many of which can not be discovered if they are measured over all the samples in each of the classes. Interestingly, in our experiments, some discovered subspace patterns have significant overlap with known cancer pathways, and some are enriched with the target gene sets of cancer-related microRNA and transcription factors. The source codes and datasets used in this paper are available at http://vk.cs.umn.edu/SDC/. 

 

 

Read More: http://www.worldscientific.com/doi/abs/10.1142/9789814295291_0017},

keywords = {Gene Expression, Transcriptome},

pubstate = {published},

tppubtype = {inproceedings}

}

In this paper, we study methods to identify differential coexpression patterns in case-control gene expression data. A differential coexpression pattern consists of a set of genes that have substantially different levels of coherence of their expression profiles across the two sample-classes, i.e., highly coherent in one class, but not in the other. Biologically, a differential coexpression patterns may indicate the disruption of a regulatory mechanism possibly caused by disregulation of pathways or mutations of transcription factors. A common feature of all the existing approaches for differential coexpression analysis is that the coexpression of a set of genes is measured on all the samples in each of the two classes, i.e., over the full-space of samples. Hence, these approaches may miss patterns that only cover a subset of samples in each class, i.e., subspace patterns, due to the heterogeneity of the subject population and disease causes. In this paper, we extend differential coexpression analysis by defining a subspace differential coexpression pattern, i.e., a set of genes that are coexpressed in a relatively large percent of samples in one class, but in a much smaller percent of samples in the other class. We propose a general approach based upon association analysis framework that allows exhaustive yet efficient discovery of subspace differential coexpression patterns. This approach can be used to adapt a family of biclustering algorithms to obtain their corresponding differential versions that can directly discover differential coexpression patterns. Using a recently developed biclustering algorithm as illustration, we perform experiments on cancer datasets which demonstrates the existence of subspace differential coexpression patterns. Permutation tests demonstrate the statistical significance for a large number of discovered subspace patterns, many of which can not be discovered if they are measured over all the samples in each of the classes. Interestingly, in our experiments, some discovered subspace patterns have significant overlap with known cancer pathways, and some are enriched with the target gene sets of cancer-related microRNA and transcription factors. The source codes and datasets used in this paper are available at http://vk.cs.umn.edu/SDC/.

Read More: http://www.worldscientific.com/doi/abs/10.1142/9789814295291_0017

Jacko, Julie A.; Adam, Terrence; Westra, Bonnie; Witrak, Marty; Berkeland, Ron; Nelson, Andrew F.; Ali, Adel L.; Johnson, Layne; Kuang, Rui; LaTour, Kathy; Potthoff, Sandra; Watters, Amy

Launching: university partnership for health informatics Inproceedings

In: Proceedings of the 1st ACM International Health Informatics Symposium, ACM 2010, ISBN: 978-1-4503-0030-8.

Abstract | Links | BibTeX | Tags:

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