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A machine learning approach for gene...

Le, Thanh Ngoc.

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A machine learning approach for gene expression analysis and applications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A machine learning approach for gene expression analysis and applications./
Author:	Le, Thanh Ngoc.
Description:	254 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Contained By:	Dissertation Abstracts International74-09B(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3562646
ISBN:	9781303103957

A machine learning approach for gene expression analysis and applications.
Le, Thanh Ngoc.

A machine learning approach for gene expression analysis and applications. - 254 p.

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.

Thesis (Ph.D.)--University of Colorado at Denver, 2013.

High-throughput microarray technology is an important and revolutionary technique used in genomics and systems biology to analyze the expression of thousands of genes simultaneously. The popular use of this technique has resulted in enormous repositories of microarray data, for example, the Gene Expression Omnibus (GEO), maintained by the National Center for Biotechnology Information (NCBI). However, an effective approach to optimally exploit these datasets in support of specific biological studies is still lacking. Specifically, an improved method is required to integrate data from multiple sources and to select only those datasets that meet an investigator's interest. In addition, to exploit the full power of microarray data, an effective method is required to determine the relationships among genes in the selected datasets and to interpret the biological meanings behind these relationships.

ISBN: 9781303103957Subjects--Topical Terms:

626642
Computer Science.

A machine learning approach for gene expression analysis and applications.
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020 $a 9781303103957
035 $a (MiAaPQ)AAI3562646
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100 1 $a Le, Thanh Ngoc. $3 2049798
245 1 2 $a A machine learning approach for gene expression analysis and applications.
300 $a 254 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
500 $a Advisers: Tom Altman; Katheleen Gardiner.
502 $a Thesis (Ph.D.)--University of Colorado at Denver, 2013.
520 $a High-throughput microarray technology is an important and revolutionary technique used in genomics and systems biology to analyze the expression of thousands of genes simultaneously. The popular use of this technique has resulted in enormous repositories of microarray data, for example, the Gene Expression Omnibus (GEO), maintained by the National Center for Biotechnology Information (NCBI). However, an effective approach to optimally exploit these datasets in support of specific biological studies is still lacking. Specifically, an improved method is required to integrate data from multiple sources and to select only those datasets that meet an investigator's interest. In addition, to exploit the full power of microarray data, an effective method is required to determine the relationships among genes in the selected datasets and to interpret the biological meanings behind these relationships.
520 $a To address these requirements, we have developed a machine learning based approach that includes: • An effective meta-analysis method to integrate microarray data from multiple sources; the method exploits information regarding the biological context of interest provided by the biologists. • A novel and effective cluster analysis method to identify hidden patterns in selected data representing relationships between genes under the biological conditions of interest. • A novel motif finding method that discovers, not only the common transcription factor binding sites of co-regulated genes, but also the miRNA binding sites associated with the biological conditions. • A machine learning-based framework for microarray data analysis with a web application to run common analysis tasks on online.
590 $a School code: 0765.
650 4 $a Computer Science. $3 626642
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0984
690 $a 0715
710 2 $a University of Colorado at Denver. $b Computer Science. $3 2049799
773 0 $t Dissertation Abstracts International $g 74-09B(E).
790 $a 0765
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3562646