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Predicting protein sub-cellular loca...

Lu, Zhiyong.

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Predicting protein sub-cellular localization from homologs using machine learning algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting protein sub-cellular localization from homologs using machine learning algorithms./
Author:	Lu, Zhiyong.
Description:	59 p.
Notes:	Source: Masters Abstracts International, Volume: 42-05, page: 1759.
Contained By:	Masters Abstracts International42-05.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ87817
ISBN:	0612878171

Predicting protein sub-cellular localization from homologs using machine learning algorithms.
Lu, Zhiyong.

Predicting protein sub-cellular localization from homologs using machine learning algorithms. - 59 p.

Source: Masters Abstracts International, Volume: 42-05, page: 1759.

Thesis (M.Sc.)--University of Alberta (Canada), 2003.

With the rapid growth of modern sequencing technology, more and more entire genomes have been completed, leading to an explosion of new gene sequence. Unfortunately, for most of these new sequences, we do not know any of their properties, such as their sub-cellular localizations. It is too time consuming to determine the properties of each sequence manually in a biological laboratory, since it typically takes months or even years to determine the properties of even a single protein sequence.

ISBN: 0612878171Subjects--Topical Terms:

626642
Computer Science.

Predicting protein sub-cellular localization from homologs using machine learning algorithms.
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100 1 $a Lu, Zhiyong. $3 1029362
245 1 0 $a Predicting protein sub-cellular localization from homologs using machine learning algorithms.
300 $a 59 p.
500 $a Source: Masters Abstracts International, Volume: 42-05, page: 1759.
500 $a Advisers: Duane Szafron; Russ Greiner.
502 $a Thesis (M.Sc.)--University of Alberta (Canada), 2003.
520 $a With the rapid growth of modern sequencing technology, more and more entire genomes have been completed, leading to an explosion of new gene sequence. Unfortunately, for most of these new sequences, we do not know any of their properties, such as their sub-cellular localizations. It is too time consuming to determine the properties of each sequence manually in a biological laboratory, since it typically takes months or even years to determine the properties of even a single protein sequence.
520 $a A much quicker alternative is to use computational techniques to make predictions in a high-throughput fashion. Proteome Analyst SUB-cellular localization server (PA-SUB) is a system that uses machine learning techniques to predict the sub-cellular localization of each protein in a proteome. This dissertation demonstrates how PA-SUB applies established machine learning techniques to make accurate predictions. It describes techniques and experiments that establish reliable training/testing datasets, significant feature extraction, efficient algorithm selection and convincing validation methods.
520 $a PA-SUB is described and the results of its application are presented along with concrete examples. Experiments are presented that compare different feature identification techniques and different classifier technologies. We obtained excellent results (approximately 90% 5-fold cross-validation accuracy) for sub-cellular localization prediction. These results are better than published results using other techniques. More generally, this dissertation demonstrates that computational machine learning techniques can be used effectively for the prediction of protein properties.
590 $a School code: 0351.
650 4 $a Computer Science. $3 626642
650 4 $a Artificial Intelligence. $3 769149
690 $a 0984
690 $a 0800
710 2 0 $a University of Alberta (Canada). $3 626651
773 0 $t Masters Abstracts International $g 42-05.
790 1 0 $a Szafron, Duane, $e advisor
790 1 0 $a Greiner, Russ, $e advisor
790 $a 0351
791 $a M.Sc.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ87817