Dna Microarray Theory

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Cancer is a major root cause of disease among human deaths in many developed countries. Cancer Prediction or classification in medical practice trusted on clinical and histopathological facts could be produce incomplete or misleading results .The DNA microarray is very useful to determine the expression level of thousands of genes simultaneously in a cell mixture [1]. DNA microarray technology has been applied to find out the accurate prediction and diagnosis of cancer. Molecular level diagnostics with gene expression profiles can offer the methodology of accurate and systematic cancer prediction. It’s very important for treatment of cancer to classify tumour accurately. Because the gene expression data generally comprise of huge number of genes, several scholars have been scrutinizing the problems of cancer classification using data mining approaches, statistical methods and machine learning algorithms to effectually evaluate these data [2].Various machine learning
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The adaptive resonance theory (ART) has been developed to avoid the stability-plasticity dilemma (SPD) in competitive networks learning. Basic ART structure consist of Comparison field and recognition field composed of neuron Vigilance parameter Reset module .COMPARISON FIELD:- It takes an input vector and transfer it to its best match in recognition field. RECOGNITION FIELD:- The cluster units , also called a competitive layer. VIGILANCE PARAMETER: - After input vector is classified, a reset module compares the strength of the recognition match to a vigilance parameter. RESET MODULE:- To control the degree of similarity of pattern placed on the same
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