Effective automated feature construction and selection for classification of biological sequences.

<h4>Background</h4>Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interact...

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Autores principales:	Uday Kamath, Kenneth De Jong, Amarda Shehu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2ded894b5ea545f58ac33024d1af601c
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spelling	oai:doaj.org-article:2ded894b5ea545f58ac33024d1af601c2021-11-25T06:08:06ZEffective automated feature construction and selection for classification of biological sequences.1932-620310.1371/journal.pone.0099982https://doaj.org/article/2ded894b5ea545f58ac33024d1af601c2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25033270/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection of regulatory regions, splice sites, exons, hypersensitive sites, and more. These problems naturally lend themselves to formulation as classification problems in machine learning. When classification is based on features extracted from the sequences under investigation, success is critically dependent on the chosen set of features.<h4>Methodology</h4>We present an algorithmic framework (EFFECT) for automated detection of functional signals in biological sequences. We focus here on classification problems involving DNA sequences which state-of-the-art work in machine learning shows to be challenging and involve complex combinations of local and distal features. EFFECT uses a two-stage process to first construct a set of candidate sequence-based features and then select a most effective subset for the classification task at hand. Both stages make heavy use of evolutionary algorithms to efficiently guide the search towards informative features capable of discriminating between sequences that contain a particular functional signal and those that do not.<h4>Results</h4>To demonstrate its generality, EFFECT is applied to three separate problems of importance in DNA research: the recognition of hypersensitive sites, splice sites, and ALU sites. Comparisons with state-of-the-art algorithms show that the framework is both general and powerful. In addition, a detailed analysis of the constructed features shows that they contain valuable biological information about DNA architecture, allowing biologists and other researchers to directly inspect the features and potentially use the insights obtained to assist wet-laboratory studies on retainment or modification of a specific signal. Code, documentation, and all data for the applications presented here are provided for the community at http://www.cs.gmu.edu/~ashehu/?q=OurTools.Uday KamathKenneth De JongAmarda ShehuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e99982 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Uday Kamath Kenneth De Jong Amarda Shehu Effective automated feature construction and selection for classification of biological sequences.
description	<h4>Background</h4>Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection of regulatory regions, splice sites, exons, hypersensitive sites, and more. These problems naturally lend themselves to formulation as classification problems in machine learning. When classification is based on features extracted from the sequences under investigation, success is critically dependent on the chosen set of features.<h4>Methodology</h4>We present an algorithmic framework (EFFECT) for automated detection of functional signals in biological sequences. We focus here on classification problems involving DNA sequences which state-of-the-art work in machine learning shows to be challenging and involve complex combinations of local and distal features. EFFECT uses a two-stage process to first construct a set of candidate sequence-based features and then select a most effective subset for the classification task at hand. Both stages make heavy use of evolutionary algorithms to efficiently guide the search towards informative features capable of discriminating between sequences that contain a particular functional signal and those that do not.<h4>Results</h4>To demonstrate its generality, EFFECT is applied to three separate problems of importance in DNA research: the recognition of hypersensitive sites, splice sites, and ALU sites. Comparisons with state-of-the-art algorithms show that the framework is both general and powerful. In addition, a detailed analysis of the constructed features shows that they contain valuable biological information about DNA architecture, allowing biologists and other researchers to directly inspect the features and potentially use the insights obtained to assist wet-laboratory studies on retainment or modification of a specific signal. Code, documentation, and all data for the applications presented here are provided for the community at http://www.cs.gmu.edu/~ashehu/?q=OurTools.
format	article
author	Uday Kamath Kenneth De Jong Amarda Shehu
author_facet	Uday Kamath Kenneth De Jong Amarda Shehu
author_sort	Uday Kamath
title	Effective automated feature construction and selection for classification of biological sequences.
title_short	Effective automated feature construction and selection for classification of biological sequences.
title_full	Effective automated feature construction and selection for classification of biological sequences.
title_fullStr	Effective automated feature construction and selection for classification of biological sequences.
title_full_unstemmed	Effective automated feature construction and selection for classification of biological sequences.
title_sort	effective automated feature construction and selection for classification of biological sequences.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/2ded894b5ea545f58ac33024d1af601c
work_keys_str_mv	AT udaykamath effectiveautomatedfeatureconstructionandselectionforclassificationofbiologicalsequences AT kennethdejong effectiveautomatedfeatureconstructionandselectionforclassificationofbiologicalsequences AT amardashehu effectiveautomatedfeatureconstructionandselectionforclassificationofbiologicalsequences
_version_	1718414153785278464

Effective automated feature construction and selection for classification of biological sequences.

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