Classification of PCR-SSCP bands in T2DM by probabilistic neural network: a reliable tool
by A.R.S. Badarinath; A. Raja Das; Sreya Mazumder; Riya Banerjee; Pratyusa Chakraborty; Radha Saraswathy
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 11, No. 4, 2015

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.

Online publication date: Sat, 27-Jun-2015

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