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Machine learning and graph theory ap...

Georgia State University.

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Machine learning and graph theory approaches for classification and prediction of protein structure.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning and graph theory approaches for classification and prediction of protein structure./
作者:	Altun, Gulsah.
面頁冊數:	116 p.
附註:	Adviser: Robert W. Harrison.
Contained By:	Dissertation Abstracts International69-04B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3308459
ISBN:	9780549564195

Machine learning and graph theory approaches for classification and prediction of protein structure.
Altun, Gulsah.

Machine learning and graph theory approaches for classification and prediction of protein structure. - 116 p.

Adviser: Robert W. Harrison.

Thesis (Ph.D.)--Georgia State University, 2008.

Index words. algorithm, machine learning, graph theory, support vector machines, feature selection, protein structure prediction.

ISBN: 9780549564195Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Machine learning and graph theory approaches for classification and prediction of protein structure.
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020 $a 9780549564195
035 $a (UMI)AAI3308459
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100 1 $a Altun, Gulsah. $3 1043958
245 1 0 $a Machine learning and graph theory approaches for classification and prediction of protein structure.
300 $a 116 p.
500 $a Adviser: Robert W. Harrison.
500 $a Source: Dissertation Abstracts International, Volume: 69-04, Section: B, page: 2406.
502 $a Thesis (Ph.D.)--Georgia State University, 2008.
520 $a Index words. algorithm, machine learning, graph theory, support vector machines, feature selection, protein structure prediction.
520 $a Recently, many methods have been proposed for the classification and prediction problems in bioinformatics. One of these problems is the protein structure prediction. Machine learning approaches and new algorithms have been proposed to solve this problem. Among the machine learning approaches, Support Vector Machines (SVM) have attracted a lot of attention due to their high prediction accuracy. Since protein data consists of sequence and structural information, another most widely used approach for modeling this structured data is to use graphs. In computer science, graph theory has been widely studied; however it has only been recently applied to bioinformatics. In this work, we introduced new algorithms based on statistical methods, graph theory concepts and machine learning for the protein structure prediction problem. A new statistical method based on z-scores has been introduced for seed selection in proteins. A new method based on finding common cliques in protein data for feature selection is also introduced, which reduces noise in the data. We also introduced new binary classifiers for the prediction of structural transitions in proteins. These new binary classifiers achieve much higher accuracy results than the current traditional binary classifiers.
590 $a School code: 0079.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
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710 2 $a Georgia State University. $3 1018518
773 0 $t Dissertation Abstracts International $g 69-04B.
790 $a 0079
790 1 0 $a Harrison, Robert W., $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3308459