Academic Journal
Multiple Sequence Alignment with Evolutionary Computation.
| Τίτλος: | Multiple Sequence Alignment with Evolutionary Computation. |
|---|---|
| Συγγραφείς: | Conrad Shyu, Luke Sheneman, James A. Foster |
| Πηγή: | Genetic Programming & Evolvable Machines; Jun2004, Vol. 5 Issue 2, p121-144, 24p |
| Περίληψη: | Abstract In this paper we provide a brief review of current work in the area of multiple sequence alignment (MSA) for DNA and protein sequences using evolutionary computation (EC). We detail the strengths and weaknesses of EC techniques for MSA. In addition, we present two novel approaches for inferring MSA using genetic algorithms. Our first novel approach utilizes a GA to evolve an optimal guide tree in a progressive alignment algorithm and serves as an alternative to the more traditional heuristic techniques such as neighbor-joining. The second novel approach facilitates the optimization of a consensus sequence with a GA using a vertically scalable encoding scheme in which the number of iterations needed to find the optimal solution is approximately the same regardless the number of sequences being aligned. We compare both of our novel approaches to the popular progressive alignment program Clustal W. Experiments have confirmed that EC constitutes an attractive and promising alternative to traditional heuristic algorithms for MSA. [ABSTRACT FROM AUTHOR] |
| Copyright of Genetic Programming & Evolvable Machines is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 22138291 RelevancyScore: 832 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 832.058959960938 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multiple Sequence Alignment with Evolutionary Computation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Conrad++Shyu%22">Conrad Shyu</searchLink><br /><searchLink fieldCode="AR" term="%22Luke++Sheneman%22">Luke Sheneman</searchLink><br /><searchLink fieldCode="AR" term="%22James+A%2E++Foster%22">James A. Foster</searchLink> – Name: TitleSource Label: Source Group: Src Data: Genetic Programming & Evolvable Machines; Jun2004, Vol. 5 Issue 2, p121-144, 24p – Name: Abstract Label: Abstract Group: Ab Data: Abstract In this paper we provide a brief review of current work in the area of multiple sequence alignment (MSA) for DNA and protein sequences using evolutionary computation (EC). We detail the strengths and weaknesses of EC techniques for MSA. In addition, we present two novel approaches for inferring MSA using genetic algorithms. Our first novel approach utilizes a GA to evolve an optimal guide tree in a progressive alignment algorithm and serves as an alternative to the more traditional heuristic techniques such as neighbor-joining. The second novel approach facilitates the optimization of a consensus sequence with a GA using a vertically scalable encoding scheme in which the number of iterations needed to find the optimal solution is approximately the same regardless the number of sequences being aligned. We compare both of our novel approaches to the popular progressive alignment program Clustal W. Experiments have confirmed that EC constitutes an attractive and promising alternative to traditional heuristic algorithms for MSA. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Genetic Programming & Evolvable Machines is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 121 Titles: – TitleFull: Multiple Sequence Alignment with Evolutionary Computation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Conrad Shyu – PersonEntity: Name: NameFull: Luke Sheneman – PersonEntity: Name: NameFull: James A. Foster IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2004 Type: published Y: 2004 Identifiers: – Type: issn-print Value: 13892576 Numbering: – Type: volume Value: 5 – Type: issue Value: 2 Titles: – TitleFull: Genetic Programming & Evolvable Machines Type: main |
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