Using recurrence quantification analysis descriptors for protein sequence classification with support vector machines - Mitra, Joydeep; Mundra, Piyushkumar; Kulkarni, B. D.; Jayaraman, Valadi K. (Journal of Biomolecular Structure & Dynamics, 2007)
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.

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%.