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• W2823132974 startingPage "477" @default.
• W2823132974 abstract "One of popular topic in bioinformatics is protein sequence analysis. The graphical representation of protein sequence is a simple and common way to visualize protein sequences. In this study, a numerical descriptive vector for a given protein sequence is calculated based on twelve physicochemical properties of amino acids (AAs) and principal component analysis (PCA). Each entry of the descriptive vector corresponds to one AA in the sequence. By this vector, an intuitive spectrum-like graphical representation of protein sequence is proposed. Squared correlation coefficient as well as moving window correlation coefficient, as a new similarity/dissimilarity measure, were used to compare different sequences. Applicability of the proposed method is assessed by analyzing the nine ND5 proteins. The results revealed the utility of the proposed method." @default.
• W2823132974 created "2018-07-19" @default.
• W2823132974 creator A5001792434 @default.
• W2823132974 creator A5038049890 @default.
• W2823132974 creator A5061980473 @default.
• W2823132974 date "2018-11-01" @default.
• W2823132974 modified "2023-10-15" @default.
• W2823132974 title "A novel graphical representation and similarity analysis of protein sequences based on physicochemical properties" @default.
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• W2823132974 doi "https://doi.org/10.1016/j.physa.2018.07.011" @default.
• W2823132974 hasPublicationYear "2018" @default.
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