Identification of defensins employing recurrence quantification analysis and random forest classifiers - Karnik, Shreyas; Prasad, Ajay; Diwevedi, Alok; Sundararajan, V.; Jayaraman, Valadi K. (Pattern Recognition and Machine Intelligence, Proceedings, 2009)
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.
3rd International Conference on Pattern Recognition and Machine Intelligence, IIT Delhi, New Delhi, INDIA, DEC 16-20, 2009

Identification of N-glycosylation sites with sequence and structural features employing random forests - Karnik, Shreyas; Mitra, Joydeep; Singh, Arunima; Kulkarni, B. D.; Sundarajan, V.; Jayaraman, Valadi K. (Pattern Recognition and Machine Intelligence, Proceedings, 2009)
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%.
3rd International Conference on Pattern Recognition and Machine Intelligence, IIT Delhi, New Delhi, INDIA, DEC 16-20, 2009