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dc.contributor.authorRaicar, Gaurav
dc.contributor.authorSaini, Harsh
dc.contributor.authorDehzangi, Abdollah
dc.contributor.authorLal, Sunil
dc.contributor.authorSharma, Alok
dc.date.accessioned2018-10-02T06:07:59Z
dc.date.available2018-10-02T06:07:59Z
dc.date.issued2016
dc.identifier.issn0022-5193en_US
dc.identifier.doi10.1016/j.jtbi.2016.05.002en_US
dc.identifier.urihttp://hdl.handle.net/10072/100298
dc.description.abstractPredicting the three-dimensional (3-D) structure of a protein is an important task in the field of bioinformatics and biological sciences. However, directly predicting the 3-D structure from the primary structure is hard to achieve. Therefore, predicting the fold or structural class of a protein sequence is generally used as an intermediate step in determining the protein's 3-D structure. For protein fold recognition (PFR) and structural class prediction (SCP), two steps are required – feature extraction step and classification step. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In this study, we explore the importance of utilizing the physicochemical properties of amino acids for improving PFR and SCP accuracies. For this, we propose a Forward Consecutive Search (FCS) scheme which aims to strategically select physicochemical attributes that will supplement the existing feature extraction techniques for PFR and SCP. An exhaustive search is conducted on all the existing 544 physicochemical attributes using the proposed FCS scheme and a subset of physicochemical attributes is identified. Features extracted from these selected attributes are then combined with existing syntactical-based and evolutionary-based features, to show an improvement in the recognition and prediction performance on benchmark datasets.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherElsevieren_US
dc.relation.ispartofpagefrom117en_US
dc.relation.ispartofpageto128en_US
dc.relation.ispartofjournalJournal of Theoretical Biologyen_US
dc.relation.ispartofvolume402en_US
dc.subject.fieldofresearchBiological Sciences not elsewhere classifieden_US
dc.subject.fieldofresearchcode069999en_US
dc.titleImproving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acidsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.hasfulltextNo Full Text


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