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

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2015-06-01 DOI:10.1504/IJBRA.2015.070115

A. Badarinath, A. Das, Sreya Mazumder, Riya Banerjee, P. Chakraborty, R. Saraswathy

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引用次数: 0

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.

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用概率神经网络分类T2DM患者PCR-SSCP频带:一个可靠的工具

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。摄影图像的检测可能包含在摄影过程中产生的噪声;因此，采用图像处理技术来降低图像噪声。首先对T2DM患者(n = 100)和对照组(n = 100)的PCR-SSCP凝胶进行等比像素拍摄，然后进行特征提取和PNN两阶段分析。通过质量训练对结果进行评价，准确率达95%，人眼分析显示突变检出率为80%。本研究为糖尿病突变分析提供了准确、快速的检测方法。该方法可推广应用于其他人类疾病的分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.