Identification of Defensins Employing Recurrence Quantification Analysis and Random Forest Classifiers

Authors:
Shreyas Karnik;Ajay Prasad;Alok Diwevedi;V. Sundararajan;V. K. Jayaraman
Affiliations:
Chemical Engineering and Process Development Division, National Chemical Laboratory, Pune, India 411008 and School of Informatics, Indiana University, Indianapolis, USA 46202;Chemical Engineering and Process Development Division, National Chemical Laboratory, Pune, India 411008;Chemical Engineering and Process Development Division, National Chemical Laboratory, Pune, India 411008;Center for Development of Advanced Computing, Pune, India 411007;Center for Development of Advanced Computing, Pune, India 411007
Venue:
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Year:
2009

Citing 2
Cited 0

Random Forests

Machine Learning
Pathway analysis using random forests classification and regression

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

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.