Dissertation/ Thesis
Evolutionary Algorithms for Multiple Sequence Alignments
| Τίτλος: | Evolutionary Algorithms for Multiple Sequence Alignments |
|---|---|
| Συγγραφείς: | Naznin, Farhana |
| Στοιχεία εκδότη: | UNSW, Sydney |
| Έτος έκδοσης: | 2011 |
| Συλλογή: | UNSW Sydney (The University of New South Wales): UNSWorks |
| Θεματικοί όροι: | Amino Acid Sequences, Deoxyribonucleic Acid, Ribonucleic Acid, Dynamic Algorithm / Programming, Pairwise Sequence Alignment, Guide Tree / Progressive Alignment Approach, Multiple Sequence Alignment, Iterative Approach, Genetic Algorithm |
| Περιγραφή: | Multiple sequence alignment (MSA) is a valuable technique for studying molecular evolution, analysing sequence structure, and drug design. To date, in the literature, many algorithms for solving the MSA problems, based on progressive, iterative and stochastic approaches, have been proposed. As their performances vary depending on the characteristics of the test problems, there is a need for an effective algorithm that consistently performs better over a range of problems. Therefore, the objective of this thesis was to develop a new algorithm for solving MSA problems more effectively. However, our systematic analyses of the existing approaches encouraged us to propose not only a new algorithm but also the sequential development of increasingly effective algorithms. Firstly, this thesis presents a new iterative method, namely MSA-IT, which has two new mechanisms: the first generates guide trees with randomly selected sequences; and the second shuffles the sequences inside these trees, within an iterative process, to generate new and better guide trees. This method is simple and fast. Its performance was tested on standard benchmark problems and, compared with well-known algorithms, was convincing. However, as its solutions made clear that there was room for further improvement, we developed another algorithm based on a genetic algorithm with a progressive alignment approach, namely GAPAM. In GAPAM, the initial population is generated using the two mechanisms used in MSA-IT, and two new genetic search operators are implemented. To test its performance on benchmark datasets, the results from GAPAM were compared with those from MSA-IT as well as the well-known algorithms. The comparisons showed that GAPAM not only provides better solutions than MSA-IT and the other algorithms for most of the test problems, but also achieves an overall better performance. However, analyses of the results uncovered the fact that, for sequences of longer length, GAPAM was trapped in local optima. To overcome this local optima problem, ... |
| Τύπος εγγράφου: | doctoral or postdoctoral thesis |
| Περιγραφή αρχείου: | application/pdf |
| Γλώσσα: | English |
| Relation: | https://hdl.handle.net/1959.4/51535; https://doi.org/10.26190/unsworks/15176 |
| DOI: | 10.26190/unsworks/15176 |
| Διαθεσιμότητα: | https://hdl.handle.net/1959.4/51535 https://unsworks.unsw.edu.au/bitstreams/8e11d1c2-0ff5-4ff1-938a-d0b70ddb1436/download https://doi.org/10.26190/unsworks/15176 |
| Rights: | open access ; https://purl.org/coar/access_right/c_abf2 ; CC BY-NC-ND 3.0 ; https://creativecommons.org/licenses/by-nc-nd/3.0/au/ ; free_to_read |
| Αριθμός Καταχώρησης: | edsbas.246EB642 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hdl.handle.net/1959.4/51535# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.246EB642 RelevancyScore: 765 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 764.654296875 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Evolutionary Algorithms for Multiple Sequence Alignments – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Naznin%2C+Farhana%22">Naznin, Farhana</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: UNSW, Sydney – Name: DatePubCY Label: Publication Year Group: Date Data: 2011 – Name: Subset Label: Collection Group: HoldingsInfo Data: UNSW Sydney (The University of New South Wales): UNSWorks – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Amino+Acid+Sequences%22">Amino Acid Sequences</searchLink><br /><searchLink fieldCode="DE" term="%22Deoxyribonucleic+Acid%22">Deoxyribonucleic Acid</searchLink><br /><searchLink fieldCode="DE" term="%22Ribonucleic+Acid%22">Ribonucleic Acid</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+Algorithm+%2F+Programming%22">Dynamic Algorithm / Programming</searchLink><br /><searchLink fieldCode="DE" term="%22Pairwise+Sequence+Alignment%22">Pairwise Sequence Alignment</searchLink><br /><searchLink fieldCode="DE" term="%22Guide+Tree+%2F+Progressive+Alignment+Approach%22">Guide Tree / Progressive Alignment Approach</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+Sequence+Alignment%22">Multiple Sequence Alignment</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+Approach%22">Iterative Approach</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+Algorithm%22">Genetic Algorithm</searchLink> – Name: Abstract Label: Description Group: Ab Data: Multiple sequence alignment (MSA) is a valuable technique for studying molecular evolution, analysing sequence structure, and drug design. To date, in the literature, many algorithms for solving the MSA problems, based on progressive, iterative and stochastic approaches, have been proposed. As their performances vary depending on the characteristics of the test problems, there is a need for an effective algorithm that consistently performs better over a range of problems. Therefore, the objective of this thesis was to develop a new algorithm for solving MSA problems more effectively. However, our systematic analyses of the existing approaches encouraged us to propose not only a new algorithm but also the sequential development of increasingly effective algorithms. Firstly, this thesis presents a new iterative method, namely MSA-IT, which has two new mechanisms: the first generates guide trees with randomly selected sequences; and the second shuffles the sequences inside these trees, within an iterative process, to generate new and better guide trees. This method is simple and fast. Its performance was tested on standard benchmark problems and, compared with well-known algorithms, was convincing. However, as its solutions made clear that there was room for further improvement, we developed another algorithm based on a genetic algorithm with a progressive alignment approach, namely GAPAM. In GAPAM, the initial population is generated using the two mechanisms used in MSA-IT, and two new genetic search operators are implemented. To test its performance on benchmark datasets, the results from GAPAM were compared with those from MSA-IT as well as the well-known algorithms. The comparisons showed that GAPAM not only provides better solutions than MSA-IT and the other algorithms for most of the test problems, but also achieves an overall better performance. However, analyses of the results uncovered the fact that, for sequences of longer length, GAPAM was trapped in local optima. To overcome this local optima problem, ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: doctoral or postdoctoral thesis – Name: Format Label: File Description Group: SrcInfo Data: application/pdf – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://hdl.handle.net/1959.4/51535; https://doi.org/10.26190/unsworks/15176 – Name: DOI Label: DOI Group: ID Data: 10.26190/unsworks/15176 – Name: URL Label: Availability Group: URL Data: https://hdl.handle.net/1959.4/51535<br />https://unsworks.unsw.edu.au/bitstreams/8e11d1c2-0ff5-4ff1-938a-d0b70ddb1436/download<br />https://doi.org/10.26190/unsworks/15176 – Name: Copyright Label: Rights Group: Cpyrght Data: open access ; https://purl.org/coar/access_right/c_abf2 ; CC BY-NC-ND 3.0 ; https://creativecommons.org/licenses/by-nc-nd/3.0/au/ ; free_to_read – Name: AN Label: Accession Number Group: ID Data: edsbas.246EB642 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.246EB642 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.26190/unsworks/15176 Languages: – Text: English Subjects: – SubjectFull: Amino Acid Sequences Type: general – SubjectFull: Deoxyribonucleic Acid Type: general – SubjectFull: Ribonucleic Acid Type: general – SubjectFull: Dynamic Algorithm / Programming Type: general – SubjectFull: Pairwise Sequence Alignment Type: general – SubjectFull: Guide Tree / Progressive Alignment Approach Type: general – SubjectFull: Multiple Sequence Alignment Type: general – SubjectFull: Iterative Approach Type: general – SubjectFull: Genetic Algorithm Type: general Titles: – TitleFull: Evolutionary Algorithms for Multiple Sequence Alignments Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Naznin, Farhana IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2011 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
| ResultId | 1 |