![]() ![]() Our system makes prediction with an accuracy of 100, 82.47, 88.81 for self-consistency test, jackknife test and independent data test respectively. Support Vector Machines are designed for four modules and prediction is made by a weighted voting system. A new biological feature, distribution of atomic composition is effectivelymore » used with, multiple physiochemical properties, amino acid composition, three part amino acid composition, and sequence similarity for predicting the subcellular location of the protein. In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information. ![]() ![]() For a computational localization prediction method to be more accurate, it should exploit all possible relevant biological features that contribute to the subcellular localization. Computational prediction of subcellular localization deals with predicting the location of a protein from its amino acid sequence. ![]() Subcellular location of protein is constructive information in determining its function, screening for drug candidates, vaccine design, annotation of gene products and in selecting relevant proteins for further studies. Protein location prediction using atomic composition and global features of the amino acid sequenceĭOE Office of Scientific and Technical Information (OSTI.GOV)Ĭherian, Betsy Sheena, E-mail: Nair, Achuthsankar S. ![]()
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