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Antifreeze Protein DetectionUsing Sequential Minimal Optimization Classifier


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Various cold-adaptedorganisms produce antifreeze proteins (AFPs), which prevent the cell fluids from freezing.AFPs haveseveral important applications in increasing freeze tolerance of crop plants,maintain the tissue in frozen condition and producing cold-hardy plants using transgenictechnology. In this paper, we proposed a novel methodfor predicting AFPs usingSequential Minimal Optimization(SMO)classifier incorporation 4 types of features:hydropathy,physicochemical properties,amino acid composition and evolutionary profile. Testedby10-fold cross validation, our proposed method gains91.8accuracy. In addition, results reveal the better performance of our method in AFPs detection in comparison to the current state-of-the-art methods

Item Type: Paper presented at a conference, workshop or other event, and published in the proceedings
Uncontrolled Keywords: antifreeze protein; Sequential Minimal Optimization; hydropathy; physicochemical properties;evolutionary profile
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Computer software
Divisions: Schools > Centre for Business, Information Technology and Enterprise > School of Information Technology
Depositing User: Reza Rafeh
Date Deposited: 11 Jan 2017 00:57
Last Modified: 21 Jul 2023 04:36

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