<?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%">Joshi, Rajani R.</style></author><author><style face="normal" font="default" size="100%">Panigrahi, Priyabrata R.</style></author><author><style face="normal" font="default" size="100%">Patil, Reshma N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dimensionality reduction in computational demarcation of protein tertiary structures</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Molecular Modeling</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Logistic regression</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal component analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein structural classes</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative features of tertiary folds</style></keyword><keyword><style  face="normal" font="default" size="100%">SCOP database</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">SPRINGER</style></publisher><pub-location><style face="normal" font="default" size="100%">233 SPRING ST, NEW YORK, NY 10013 USA</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">2741-2754</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Predictive classification of major structural families and fold types of proteins is investigated deploying logistic regression. Only five to seven dimensional quantitative feature vector representations of tertiary structures are found adequate. Results for benchmark sample of non-homologous proteins from SCOP database are presented. Importance of this work as compared to homology modeling and best-known quantitative approaches is highlighted.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">1.984
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