Protein structure prediction using robust principal component analysis and support vector machine

Zakaria, Nur Aini and Ali Shah, Zuraini and Kasim, Shahreen (2020) Protein structure prediction using robust principal component analysis and support vector machine. International Journal of Data Science, 1 (1). pp. 14-17. ISSN 2722-2039

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Existence of bioinformatics is to increase the further understanding of biological process. Proteins structure is one of the major challenges in structural bioinformatics. With former knowledge of the structure, the quality of secondary structure, prediction of tertiary structure, and prediction function of amino acid from its sequence increase significantly. Recently, the gap between sequence known and structure known proteins had increase dramatically. So it is compulsory to understand on proteins structure to overcome this problem so further functional analysis could be easier. The research applying RPCA algorithm to extract the essential features from the original highdimensional input vectors. Then the process followed by experimenting SVM with RBF kernel. The proposed method obtains accuracy by 84.41% for training dataset and 89.09% for testing dataset. The result then compared with the same method but PCA was applied as the feature extraction. The prediction assessment is conducted by analyzing the accuracy and number of principal component selected. It shows that combination of RPCA and SVM produce a high quality classification of protein structure

Item Type: Article
Uncontrolled Keywords: protein structure prediction; RPCA; robust principal component analysis; support vector machine
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Computer Science and Information Technology > Department of Web Technology
Depositing User: Miss Afiqah Faiqah Mohd Hafiz
Date Deposited: 27 Jan 2022 06:21
Last Modified: 27 Jan 2022 06:21

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