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.
Full text not available from this repository.
Official URL: https://icb5.ut.ac.ir/paper?manu=12883
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 |
| Deposited By: | |
| Deposited On: | 11 Jan 2017 00:57 |
| Last Modified: | 19 Dec 2018 22:50 |
Repository Staff Only: item control page
