<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mundra, Piyushkumar</style></author><author><style face="normal" font="default" size="100%">Kumar, Madhan</style></author><author><style face="normal" font="default" size="100%">Kumar, K. Krishna</style></author><author><style face="normal" font="default" size="100%">Jayaraman, Valadi K.</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Bhaskar D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using pseudo amino acid composition to predict protein subnuclear localization: approached with PSSM</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition Letters</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">factor solution score</style></keyword><keyword><style  face="normal" font="default" size="100%">multiclass SVM</style></keyword><keyword><style  face="normal" font="default" size="100%">nuclear protein</style></keyword><keyword><style  face="normal" font="default" size="100%">PSSM</style></keyword><keyword><style  face="normal" font="default" size="100%">subnuclear localization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">OCT</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">13</style></number><publisher><style face="normal" font="default" size="100%">ELSEVIER SCIENCE BV</style></publisher><pub-location><style face="normal" font="default" size="100%">PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS</style></pub-location><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">1610-1615</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Identification of Nuclear protein localization assumes significance as it can provide in depth insight for genome regulation and function annotation of novel proteins. A multiclass SVM classifier with various input features was employed for nuclear protein compartment identification. The input features include factor solution scores and evolutionary information (position specific scoring matrix (PSSM) score) apart from conventional dipeptide composition and pseudo amino acid composition. All the SVM classifiers with different sets of input features performed better than the previously available prediction classifiers. The jack-knife success rate thus obtained on the benchmark dataset constructed by Shen and Chou [Shen, H.B., Chou, K.C., 2005, Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem. Biophys. Res. Commun. 337, 752-756] is 71.23%, indicating that the novel pseudo amino acid composition approach with PSSM and SVM classifier is very promising and may at least play a complimentary role to the existing methods. (c) 2007 Elsevier B.V. All rights reserved.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">13</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;1.586&lt;/p&gt;</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mitra, Joydeep</style></author><author><style face="normal" font="default" size="100%">Mundra, Piyushkumar</style></author><author><style face="normal" font="default" size="100%">Kulkarni, B. D.</style></author><author><style face="normal" font="default" size="100%">Jayaraman, Valadi K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using recurrence quantification analysis descriptors for protein sequence classification with support vector machines</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Biomolecular Structure &amp; Dynamics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">DEC</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">ADENINE PRESS</style></publisher><pub-location><style face="normal" font="default" size="100%">2066 CENTRAL AVE, SCHENECTADY, NY 12304 USA</style></pub-location><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">289-297</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this work, we integrate a non-linear signal analysis method, recurrence quantification analysis (RQA), with the well-known machine-learning algorithm, support vector machines for the binary classification of protein sequences. Two different classification problems were selected, discriminating between aggregating and non-aggregating proteins and mostly disordered and completely ordered proteins, respectively. It has also been shown that classification performance of SVM models improve on selection of the most informative RQA descriptors as SVM input features.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">2.3</style></custom4></record></records></xml>