Identification of coding and non-coding sequences using local holder exponent formalism - Kulkarni, O. C.; Vigneshwar, R.; Jayaraman, Valadi K.; Kulkarni, B. D. (Bioinformatics, 2005)
Motivation: Accurate prediction of genes in genomes has always been a challenging task for bioinformaticians and computational biologists. The discovery of existence of distinct scaling relations in coding and non-coding sequences has led to new perspectives in the understanding of the DNA sequences. This has motivated us to exploit the differences in the local singularity distributions for characterization and classification of coding and non-coding sequences. Results: The local singularity density distribution in the coding and non-coding sequences of four genomes was first estimated using the wavelet transform modulus maxima methodology. Support vector machines classifier was then trained with the extracted features. The trained classifier is able to provide an average test accuracy of 97.7%. The local singularity features in a DNA sequence can be exploited for successful identification of coding and non-coding sequences.

Improved time series prediction with a new method for selection of model parameters - Jade, A. M.; Jayaraman, Valadi K.; Kulkarni, B. D. (Journal of Physics A-Mathematical and General, 2006)
A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Holder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data.

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