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dc.contributor.authorUllah, Ahammeden_US
dc.date.accessioned2016-10-11T05:37:47Z
dc.date.available2016-10-11T05:37:47Z
dc.date.issued2015en_US
dc.identifier.otherHPU4160586en_US
dc.identifier.urihttps://lib.hpu.edu.vn/handle/123456789/23667en_US
dc.description.abstractAb initio protein folding simulation largely depends on knowledge-based energy functions that are derived from known protein structures using statistical methods. These knowledge-based energy functions provide us with a good approximation of real protein energetics. However, these energy functions are not very informative for search algorithms and fail to distinguish the types of amino acid interactions that contribute largely to the energy function from those that do not. As a result, search algorithms frequently get trapped into the local minimaen_US
dc.format.extent21 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectComputer scienceen_US
dc.subjectBioinformaticsen_US
dc.subjectComputational biologyen_US
dc.subjectArtificial intelligenceen_US
dc.subjectProtein structure predictionen_US
dc.titleEfficient conformational space exploration in ab initioprotein folding simulationen_US
dc.typeArticleen_US
dc.size822KBen_US
dc.departmentEducationen_US


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