Antifreeze Protein DetectionUsing Sequential Minimal Optimization Classifier

Eslami, Morteza and Zahiri, Javad and Rafeh, Reza and Azizi, Masoud (2014) Antifreeze Protein DetectionUsing Sequential Minimal Optimization Classifier. The 5th Iranian Conference on Bioinformatics, Tehran, Iran, 20-22 May 2014.

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Abstract or Summary

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
Keywords that describe the item: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
ID Code:5136
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Deposited On:11 Jan 2017 00:57
Last Modified:19 Dec 2018 22:50

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