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首页> 外文期刊>International journal of bioinformatics research and applications >Classification of PCR-SSCP bands in T2DM by probabilistic neural network: A reliable tool
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Classification of PCR-SSCP bands in T2DM by probabilistic neural network: A reliable tool

机译：概率神经网络在T2DM中对PCR-SSCP条带进行分类的可靠工具

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摘要

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.
机译：概率神经网络（PNN）是一种统计算法，由一组多类数据组成。人眼检测DNA突变的传统方法可能无法检测到PCR-SSCP条带的微小变化，这可能导致假阳性或假阴性结果。通过摄影图像进行的检测可能包含在摄影时引起的眩光（噪点）;因此，图像处理技术被用来减少图像噪声。首先以相等的像素比例拍摄T2DM患者（n = 100）和对照（n = 100）的PCR-SSCP凝胶，然后进行两阶段分析：特征提取和PNN。通过质量培训对结果进行评估，准确性高达95％，人眼分析显示80％的突变检测率。这项研究被证明是非常可靠的，并且可以为糖尿病突变分析提供准确而快速的检测。该方法可扩展用于其他人类疾病的分析。

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