東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

A mixture of experts approach to pro...

MacDonald, Ian M.

Linked to FindBook

Google Book

Amazon

博客來

A mixture of experts approach to protein structural domain boundary classification.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A mixture of experts approach to protein structural domain boundary classification./
Author:	MacDonald, Ian M.
Description:	201 p.
Notes:	Adviser: George Berg.
Contained By:	Dissertation Abstracts International68-03B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3254874

A mixture of experts approach to protein structural domain boundary classification.
MacDonald, Ian M.

A mixture of experts approach to protein structural domain boundary classification. - 201 p.

Adviser: George Berg.

Thesis (Ph.D.)--State University of New York at Albany, 2007.

The prediction of protein structural domains and their boundaries from amino acid sequence data is an open problem of interest to the bioinformatics community. Determining a protein's structural domains experimentally can be very difficult. The assignment of structural domains relies on the time-consuming process of determining the three-dimensional structure. There is a strong potential for computational methods to aid in this and other tasks in structural molecular biology. Computational methods have aided in the prediction of secondary structure and function and the potential exists to predict structural domains from amino acid sequence data. Efforts from other researchers, along with the results presented here indicate that some boundaries can indeed be predicted from the amino acid sequence alone. A new machine-learning based architecture is introduced to predict structural domain boundaries (also referred to as inter-domain linker regions), as defined in a CATH-classified dataset. The key feature of this architecture is a Mixture of Experts (MoE) model. The MoE provides the ability to combine the predictions of individual classifiers, such as artificial neural networks, support vector machines, and naive Bayes classifiers, so as to capitalize on each predictor's strengths and weaknesses.Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

A mixture of experts approach to protein structural domain boundary classification.
LDR:02180nam 2200265 a 45 001 947539
005 20110524
008 110524s2007 ||||||||||||||||| ||eng d
035 $a (UMI)AAI3254874
035 $a AAI3254874
040 $a UMI $c UMI
100 1 $a MacDonald, Ian M. $3 1271007
245 1 2 $a A mixture of experts approach to protein structural domain boundary classification.
300 $a 201 p.
500 $a Adviser: George Berg.
500 $a Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1731.
502 $a Thesis (Ph.D.)--State University of New York at Albany, 2007.
520 $a The prediction of protein structural domains and their boundaries from amino acid sequence data is an open problem of interest to the bioinformatics community. Determining a protein's structural domains experimentally can be very difficult. The assignment of structural domains relies on the time-consuming process of determining the three-dimensional structure. There is a strong potential for computational methods to aid in this and other tasks in structural molecular biology. Computational methods have aided in the prediction of secondary structure and function and the potential exists to predict structural domains from amino acid sequence data. Efforts from other researchers, along with the results presented here indicate that some boundaries can indeed be predicted from the amino acid sequence alone. A new machine-learning based architecture is introduced to predict structural domain boundaries (also referred to as inter-domain linker regions), as defined in a CATH-classified dataset. The key feature of this architecture is a Mixture of Experts (MoE) model. The MoE provides the ability to combine the predictions of individual classifiers, such as artificial neural networks, support vector machines, and naive Bayes classifiers, so as to capitalize on each predictor's strengths and weaknesses.
590 $a School code: 0668.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a State University of New York at Albany. $3 769258
773 0 $t Dissertation Abstracts International $g 68-03B.
790 $a 0668
790 1 0 $a Berg, George, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3254874