Publications – Kuang Lab

@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

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, vol. 40, no. 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}

}