caa a22 4500 463186605 CHVBK 20180406164855.0 cr unu---uuuuu 170326e20071101xx s 000 0 eng 10.1007/s00726-007-0496-1 doi (NATIONALLICENCE)springer-10.1007/s00726-007-0496-1 Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes [Elektronische Daten] [T.-L. Zhang, Y.-S. Ding] Summary.: Compared with the conventional amino acid composition (AA), the pseudo amino acid composition (PseAA) as originally introduced by Chou can incorporate much more information of a protein sequence; this remarkably enhances the power to use a discrete model for predicting various attributes of a protein. In this study, based on the concept of Chou's PseAA, a 46-D (dimensional) PseAA was formulated to represent the sample of a protein and a new approach based on binary-tree support vector machines (BTSVMs) was proposed to predict the protein structural class. BTSVMs algorithm has the capability in solving the problem of unclassifiable data points in multi-class SVMs. The results by both the 10-fold cross-validation and jackknife tests demonstrate that the predictive performance using the new PseAA (46-D) is better than that of AA (20-D), which is widely used in many algorithms for protein structural class prediction. The results obtained by the new approach are quite encouraging, indicating that it can at least play a complimentary role to many of the existing methods and is a useful tool for predicting many other protein attributes as well. Springer-Verlag, 2007 Keywords: Protein structure classes - Pseudo amino acid composition - Correlation of amino acid - Hydrophobic amino acid couple - Binary tree support vector machines nationallicence BTSVM : binary-tree support vector machine nationallicence PseAA : pseudo amino acid composition nationallicence SVM : support vector machines nationallicence Zhang T.-L College of Information Sciences and Technology, Donghua University, Shanghai, China aut Ding Y.-S College of Information Sciences and Technology, Donghua University, Shanghai, China aut Amino Acids Springer-Verlag 33/4(2007-11-01), 623-629 0939-4451 33:4<623 2007 33 726 https://doi.org/10.1007/s00726-007-0496-1 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00726-007-0496-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhang T.-L College of Information Sciences and Technology, Donghua University, Shanghai, China aut NATIONALLICENCE 700 1- Ding Y.-S College of Information Sciences and Technology, Donghua University, Shanghai, China aut NATIONALLICENCE 773 0- Amino Acids Springer-Verlag 33/4(2007-11-01), 623-629 0939-4451 33:4<623 2007 33 726 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer