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dc.contributor.authorRashid, MA
dc.contributor.authorShatabda, S
dc.contributor.authorNewton, MAH
dc.contributor.authorHoque, MT
dc.contributor.authorPham, DN
dc.contributor.authorSattar, A
dc.contributor.editorSanjay Ranka
dc.date.accessioned2018-03-26T01:31:13Z
dc.date.available2018-03-26T01:31:13Z
dc.date.issued2012
dc.date.modified2013-08-22T23:39:17Z
dc.identifier.isbn9781450316705
dc.identifier.refurihttp://www.cse.buffalo.edu/ACM-BCB2012/
dc.identifier.doi10.1145/2382936.2383043
dc.identifier.urihttp://hdl.handle.net/10072/49839
dc.description.abstractProtein structure prediction is a challenging optimisation problem to the computer scientists. A large number of ex- isting (meta-)heuristic search algorithms attempt to solve the problem by exploring possible structures and finding the one with minimum free energy. However, these algo- rithms often get stuck in local minima and thus perform poorly on large sized proteins. In this paper, we present a random-walk based stagnation recovery approach. We tested our approach on tabu-based local search as well as population based genetic algorithms. The experimental results show that, random-walk is very effective for escaping from local minima for protein structure prediction on face- centred-cubic lattice and hydrophobic-polar energy model.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent257954 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherACM
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencenameACM BCB 2012
dc.relation.ispartofconferencetitle2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012
dc.relation.ispartofdatefrom2012-10-07
dc.relation.ispartofdateto2012-10-10
dc.relation.ispartoflocationOrlando, Florida, United States
dc.relation.ispartofpagefrom620
dc.relation.ispartofpageto622
dc.rights.retentionY
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchcode460299
dc.titleRandom-Walk: A Stagnation Recovery Technique for Simplified Protein Structure Prediction
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© ACM 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Random-Walk: A Stagnation Recovery Technique for Simplified Protein Structure Prediction, ISBN 978-1-4503-1670-5, http://dx.doi.org/10.1145/2382936.2383043
gro.date.issued2012
gro.hasfulltextFull Text
gro.griffith.authorSattar, Abdul
gro.griffith.authorRashid, Mahmood A.


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    Contains papers delivered by Griffith authors at national and international conferences.

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