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Effective Automated Feature Construction and Selection for Classification of Biological Sequences

Show simple item record Kamath, Uday De Jong, Kenneth Shehu, Amarda 2015-09-10T18:06:53Z 2015-09-10T18:06:53Z 2014-07-17
dc.identifier.citation Kamath U, De Jong K, Shehu A (2014) Effective Automated Feature Construction and Selection for Classification of Biological Sequences. PLoS ONE 9(7): e99982. doi:10.1371/journal.pone.0099982 en_US
dc.description.abstract Background 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. Methodology 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. Results 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​s.
dc.description.sponsorship Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund. en_US
dc.language.iso en_US en_US
dc.publisher Public Library of Science en_US
dc.rights Attribution 3.0 United States *
dc.rights.uri *
dc.subject DNA sequence analysis en_US
dc.subject Alu elements en_US
dc.subject sequence motif analysis en_US
dc.subject sequence analysis en_US
dc.subject kernel methods en_US
dc.subject Machine learning en_US
dc.subject algorithms en_US
dc.subject nucleotide sequencing en_US
dc.title Effective Automated Feature Construction and Selection for Classification of Biological Sequences en_US
dc.type Article en_US

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