Identification of defensins employing recurrence quantification analysis and random forest classifiers

TitleIdentification of defensins employing recurrence quantification analysis and random forest classifiers
Publication TypeJournal Article
Year of Publication2009
AuthorsKarnik, S, Prasad, A, Diwevedi, A, Sundararajan, V, Jayaraman, VK
Secondary AuthorsChaudhury, S, Mitra, S, Murthy, CA, Sastry, PS, Pal, SK
JournalPattern Recognition and Machine Intelligence, Proceedings
Volume5909
Pagination152-157
Date PublishedDEC
ISBN Number978-3-642-11163-1
ISSN0302-9743
Abstract

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.

Type of Journal (Indian or Foreign)Foreign
Impact Factor (IF)2.607
Divison category: 
Chemical Engineering & Process Development