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• W2021190068 abstract "The domain boundary prediction is an important task for functional classification of proteins, a homology based protein structure prediction and the high throughput structural genomics. A technique is proposed to predict protein domain boundaries using predicted secondary structure solvent accessibility and physicochemical properties of Amino Acids along protein chain. Each amino acid is represented by a set of physicochemical properties from AAIndex database. Support Vector Machine is used as a two class classifier where maximum prediction accuracy is observed by varying kernel functions and appropriate parameters. To improve the accuracy of prediction of multiple domains in protein chains, a Support Vector Machine is used as the classifier and physicochemical properties of proteins are used in this paper. It is tested on target proteins and achieves significant precision and recall." @default.
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• W2021190068 date "2014-03-01" @default.
• W2021190068 modified "2023-10-16" @default.
• W2021190068 title "Prediction of domain boundaries in protein sequences using predicted secondary structure and physicochemical properties of amino acids" @default.
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• W2021190068 doi "https://doi.org/10.1109/iccpct.2014.7054913" @default.
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