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- W2556587718 abstract "Classification of tumor into its subtypes is a critical issue these days in tumor diagnosis and treatment. The correct classification of tumor into its subtypes leads to the appropriate treatment. Traditional methods classify cancers based on morphological appearance that leads to misclassification because of the similar appearance of sub-types of cancer. To overcome this problem of misclassification, gene expression profiles of genes are used. Gene expression profiling of genes using DNA microarray data plays an important role in classification of tumor. But microarray data consists of genes expressions of thousands of genes so a method is required to reduce the dimensionality of data. In this paper, t-test scoring method is used for selecting important genes and artificial neural network is applied for the classification process. Resilient backpropagation is used as training algorithm. The experimental method employed in this paper shows that the proposed method classifies the data with more accuracy using eight genes only. So it has low computational complexity because of elimination of irrelevant features and high accuracy." @default.
- W2556587718 created "2016-11-30" @default.
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- W2556587718 date "2016-09-01" @default.
- W2556587718 modified "2023-09-23" @default.
- W2556587718 title "Gene selection for tumor classification using resilient backpropagation neural network" @default.
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- W2556587718 doi "https://doi.org/10.1109/icaccaf.2016.7748988" @default.
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