Use of Neural Network in the field of Bioinformatics for prediction of cancer
Keywords:
Multi-class classification, Principal component Analysis, Artificial Neural Network, Backpropagation Algorithm, cancer classification and diagnostic prediction of cancerAbstract
The purpose of this analysis was to develop a method for classifying cancers to specific diagnostic
categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANN by
using the small, round blue-cell tumors (SRBCTs) as the model. These cancers belong to four distinct diagnostic
categories and usually present diagnostic dilemmas in medical study. As their name implies, these cancers are difficult to
distinguish by light microscopy, and currently no single test can accurately distinguish these types of cancers. The ANN
properly classified the whole samples and identified the genes most relevant to the classification. To test the ability of the
trained ANN models to identify SRBCTs, we examined additional blinded samples that were not previously used for the
training purpose, and correctly classified them in all cases. This study demonstrates the potential applications of these
methods for tumor diagnosis and the identification of candidate targets for therapy.
