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Network-based learning algorithms fo...

Hwang, Tae Hyun.

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Network-based learning algorithms for understanding human disease.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Network-based learning algorithms for understanding human disease./
Author:	Hwang, Tae Hyun.
Description:	115 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .
Contained By:	Dissertation Abstracts International72-06B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3449145
ISBN:	9781124556598

Network-based learning algorithms for understanding human disease.
Hwang, Tae Hyun.

Network-based learning algorithms for understanding human disease. - 115 p.

Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .

Thesis (Ph.D.)--University of Minnesota, 2011.

Advances in genomics, proteomics and molecular pathology with the use of high-throughput technologies, have produced vast datasets identifying thousands of genes whose genomic changes differ in diseased versus normal samples. Many statistical and machine learning methods have been developed to discover biomarkers with potential clinical value, but building reliable learning models for the discovery of biomarkers for prediction of clinical outcomes using high-throughput dataset is still a key challenge in genomic research. This thesis introduces network-based learning algorithms to better utilize large-scale genomic data, and to integrate data with biological prior knowledge to understand the role of genetic changes in human diseases. The first method, NetProp (Network Propagation), is a graph-based semi-supervised feature classification algorithm to identify discriminative biomarkers by learning on bipartite graphs in the analysis of high dimensional genomic data. The second method, HyperPrior, is a hypergraph-based semi-supervised learning algorithm to integrate genomic data with the known biological prior knowledge for biomarker identification and patient's outcome prediction. The third method, MINProp, is a general graph-based learning algorithm to integrate multiple genomic and network data for disease gene discovery. While the method could be applied to discover candidate biomarkers in a high-throughput genomic study, validating the candidate biomarkers is another challenging problem in genomic research. To address this, we introduce a network-based method, rcNet (rank coherence in Network), to elucidate the associations between disease and genes. We applied these methods to large and various real datasets including microarray gene expression profiles, single nucleotide polymorphisms (SNPs), and DNA copy number variations. Our methods identified novel biomarkers with clinical or biological relevance with the disease, as well as achieved competitive classification performance compared with other baseline methods. Our method also successfully validated the associations between diseases and potential disease-causing genes discovered from high-throuput studies. The results indicate that the method that explore the global topological information in the networks, and integrate data with biological prior knowledge could help to discover genetic determinants of human disease, and reveal underlying biological principles of human disease.

ISBN: 9781124556598Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Network-based learning algorithms for understanding human disease.
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020 $a 9781124556598
035 $a (UMI)AAI3449145
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100 1 $a Hwang, Tae Hyun. $3 1682266
245 1 0 $a Network-based learning algorithms for understanding human disease.
300 $a 115 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .
500 $a Adviser: Rui Kuang.
502 $a Thesis (Ph.D.)--University of Minnesota, 2011.
520 $a Advances in genomics, proteomics and molecular pathology with the use of high-throughput technologies, have produced vast datasets identifying thousands of genes whose genomic changes differ in diseased versus normal samples. Many statistical and machine learning methods have been developed to discover biomarkers with potential clinical value, but building reliable learning models for the discovery of biomarkers for prediction of clinical outcomes using high-throughput dataset is still a key challenge in genomic research. This thesis introduces network-based learning algorithms to better utilize large-scale genomic data, and to integrate data with biological prior knowledge to understand the role of genetic changes in human diseases. The first method, NetProp (Network Propagation), is a graph-based semi-supervised feature classification algorithm to identify discriminative biomarkers by learning on bipartite graphs in the analysis of high dimensional genomic data. The second method, HyperPrior, is a hypergraph-based semi-supervised learning algorithm to integrate genomic data with the known biological prior knowledge for biomarker identification and patient's outcome prediction. The third method, MINProp, is a general graph-based learning algorithm to integrate multiple genomic and network data for disease gene discovery. While the method could be applied to discover candidate biomarkers in a high-throughput genomic study, validating the candidate biomarkers is another challenging problem in genomic research. To address this, we introduce a network-based method, rcNet (rank coherence in Network), to elucidate the associations between disease and genes. We applied these methods to large and various real datasets including microarray gene expression profiles, single nucleotide polymorphisms (SNPs), and DNA copy number variations. Our methods identified novel biomarkers with clinical or biological relevance with the disease, as well as achieved competitive classification performance compared with other baseline methods. Our method also successfully validated the associations between diseases and potential disease-causing genes discovered from high-throuput studies. The results indicate that the method that explore the global topological information in the networks, and integrate data with biological prior knowledge could help to discover genetic determinants of human disease, and reveal underlying biological principles of human disease.
590 $a School code: 0130.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a University of Minnesota. $b Computer Science. $3 1018528
773 0 $t Dissertation Abstracts International $g 72-06B.
790 1 0 $a Kuang, Rui, $e advisor
790 1 0 $a Kumar, Vipin $e committee member
790 1 0 $a Myers, Chad $e committee member
790 1 0 $a Wiggle, Dennis $e committee member
790 $a 0130
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3449145