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Predicting protein function using se...

Columbia University.

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Predicting protein function using sequence derived features selected by genetic algorithms.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Predicting protein function using sequence derived features selected by genetic algorithms./
Author:	Kernytsky, Andrew.
Description:	138 p.
Notes:	Source: Dissertation Abstracts International, Volume: 69-03, Section: B, page: 1401.
Contained By:	Dissertation Abstracts International69-03B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3305239
ISBN:	9780549514596

Predicting protein function using sequence derived features selected by genetic algorithms.
Kernytsky, Andrew.

Predicting protein function using sequence derived features selected by genetic algorithms. - 138 p.

Source: Dissertation Abstracts International, Volume: 69-03, Section: B, page: 1401.

Thesis (Ph.D.)--Columbia University, 2008.

Large scale sequencing of genomes has created a peculiar problem for biology: there is now a glut of information in the form of nucleotide sequences, but deciphering the higher level annotations buried in the nucleotides remains a problem that is unsolved to varying extents. One aspect of this problem is deducing the function of a protein from its sequence. This is an important challenge because the number of raw protein sequences far surpasses the number of well characterized proteins. We address this problem by using computational methods to predict protein function from sequence derived features.

ISBN: 9780549514596Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Predicting protein function using sequence derived features selected by genetic algorithms.
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100 1 $a Kernytsky, Andrew. $3 1019103
245 1 0 $a Predicting protein function using sequence derived features selected by genetic algorithms.
300 $a 138 p.
500 $a Source: Dissertation Abstracts International, Volume: 69-03, Section: B, page: 1401.
502 $a Thesis (Ph.D.)--Columbia University, 2008.
520 $a Large scale sequencing of genomes has created a peculiar problem for biology: there is now a glut of information in the form of nucleotide sequences, but deciphering the higher level annotations buried in the nucleotides remains a problem that is unsolved to varying extents. One aspect of this problem is deducing the function of a protein from its sequence. This is an important challenge because the number of raw protein sequences far surpasses the number of well characterized proteins. We address this problem by using computational methods to predict protein function from sequence derived features.
520 $a We describe a method for the prediction of protein function in terms of enzymatic activity classification (Enzyme Commission numbers) using only the protein sequence. The method begins by generating sequence derived features for a protein that range from the amino acid composition to predicted features such as secondary structure and solvent accessibility. In order to capture the local environment surrounding a key residue---a residue involved in catalysis, for example---the method searches for combinations of these features that have predictive power when they occur at the same residue. The learning algorithm may find that a particular amino acid residue is a good indicator of some protein function when another sequence derived feature indicates that the residue is predicted to be at the surface of a protein or to be in a beta sheet secondary structure element.
520 $a These predictive combinations of features are detected by a genetic algorithm used as a wrapper around a neural network. By incorporating features in the environment surrounding a single residue, the method may be seen as a specialized motif detector that detects instances of these combined features that are correlated with protein function. We evaluate the performance of this method across 59 enzymatic activity classes and find that the genetic algorithm based selection of feature combinations is able to significantly increase the predictive power of the method.
590 $a School code: 0054.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Chemistry, Biochemistry. $3 1017722
650 4 $a Computer Science. $3 626642
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710 2 $a Columbia University. $3 571054
773 0 $t Dissertation Abstracts International $g 69-03B.
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