Title: Classification of PCR-SSCP bands in T2DM by probabilistic neural network: a reliable tool

Authors: A.R.S. Badarinath; A. Raja Das; Sreya Mazumder; Riya Banerjee; Pratyusa Chakraborty; Radha Saraswathy

Addresses: School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India ' School of Advanced Sciences, VIT University, Vellore 632014, Tamil Nadu, India ' School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India ' School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India ' School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India ' School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India; Biomedical Genetics Research Laboratory (BMGRL), School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India

Abstract: A Probabilistic Neural Network (PNN) is a statistical algorithm and consists of a grouping of multi-class data. The conventional method of detection of DNA mutations by the human eye may not detect the minute variations in PCR-SSCP bands, which may lead to false positive or false negative results. The detection by photographic images may contain a blare (noise) caused during the time of photography; therefore, image processing techniques were used to reduce image noise. PCR-SSCP gels of T2DM patients (n = 100) and controls (n = 100) were initially photographed with equal ratio of pixels and later subjected to a two-stage analysis: feature extraction and PNN. The evaluation of the results was done by quality training and the accuracy was up to 95%, and the human eye analysis showed 80% mutation detection rate. This study proves to be very reliable and gives accurate and fast detection for mutation analysis in diabetes. This method could be extended for analysis in other human diseases.

Keywords: PCR-SSCP; polymerase chain reaction PCR; single stand confirmation polymorphism; SSCP; T2DM; type II diabetes mellitus; PNNs; probabilistic neural networks; feature extraction; classification; PCR-SSCP bands; bioinformatics; DNA mutations; diabetics; image processing; image noise; mutation detection rate.

DOI: 10.1504/IJBRA.2015.070115

International Journal of Bioinformatics Research and Applications, 2015 Vol.11 No.4, pp.308 - 314

Received: 14 Jan 2014
Accepted: 25 Jun 2014
Published online: 27 Jun 2015 *

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