01311nas a2200181   4500008004100000022001400041245012200055210006900177260006800246300001200314490000700326520058000333100001900913700002400932700002100956700002600977856012601003       2007                        eng d  a0739-110200aUsing recurrence quantification analysis descriptors for protein sequence classification with support vector machines0 aUsing recurrence quantification analysis descriptors for protein  a2066 CENTRAL AVE, SCHENECTADY, NY 12304 USAbADENINE PRESScDEC  a289-2970 v253 a<p>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.</p>1 aMitra, Joydeep1 aMundra, Piyushkumar1 aKulkarni, B., D.1 aJayaraman, Valadi, K.  uhttps://library.ncl.res.in/content/using-recurrence-quantification-analysis-descriptors-protein-sequence-classification-001418nas a2200277   4500008004100000020002200041022001400063245010700077210006900184260006800253300001200321490000900333520046000342100002000802700001900822700001900841700002100860700001900881700002600900700001800926700001400944700001900958700001900977700001600996856012801012       2009                        eng d  a978-3-642-11163-1  a0302-974300aIdentification of N-glycosylation sites with sequence and structural features employing random forests0 aIdentification of Nglycosylation sites with sequence and structu  aHEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANYbISI KolkatacDEC  a146-1510 v59093 a<p>N-Glycosylation plays a very important role in various processes like quality control of proteins produced in ER, transport of proteins and in disease control. The experimental elucidation of N-Glycosylation sites is expensive and laborious process. In this work we build models for identification of potential N-Glycosylation sites in proteins based on sequence and structural features. The best model has cross validation accuracy rate of 72.81%.</p>1 aKarnik, Shreyas1 aMitra, Joydeep1 aSingh, Arunima1 aKulkarni, B., D.1 aSundarajan, V.1 aJayaraman, Valadi, K.1 aChaudhury, S.1 aMitra, S.1 aMurthy, C., A.1 aSastry, P., S.1 aPal, S., K.  uhttps://library.ncl.res.in/content/identification-n-glycosylation-sites-sequence-and-structural-features-employing-random-0