01421nas a2200265   4500008004100000020002200041022001400063245010700077210006900184260006800253300001200321490000900333520049500342100002000837700001700857700001900874700002100893700002600914700001800940700001400958700001900972700001900991700001601010856012901026       2009                        eng d  a978-3-642-11163-1  a0302-974300aIdentification of defensins employing recurrence quantification analysis and random forest classifiers0 aIdentification of defensins employing recurrence quantification   aHEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANYbISI KolkatacDEC  a152-1570 v59093 a<p>Defensins represent a class of antimicrobial peptides synthesized in the body acting against various microbes. In this paper we study defensins using a non-linear signal analysis method Recurrence Quantication Analysis (RQA). We used the descriptors calculated employing RQA for the classification of defensins with Random Forest Classifier. The RQA descriptors were able to capture patterns peculiar to defensins leading to an accuracy rate of 78.12% using 10-fold cross validation.</p>1 aKarnik, Shreyas1 aPrasad, Ajay1 aDiwevedi, Alok1 aSundararajan, 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-defensins-employing-recurrence-quantification-analysis-and-random-forest-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