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Received April 7, 2015
Accepted November 26, 2015
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
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Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers
National Institute of Technology Raipur, Raipur-492010, India
Korean Journal of Chemical Engineering, April 2016, 33(4), 1318-1324(7), 10.1007/s11814-015-0255-z
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Abstract
Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarraybased gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.
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