Application of Artificial Neural Networks in Cancer Classification and Diagnosis Prediction of a Subtype of Lymphoma Based on Gene Expression Profile

L Ziaei, AR Mehri, M Salehi


Background: Diffuse Large B-cell Lymphoma (DLBCL) is the most common subtype of non-Hodgkin’s Lymphoma. DLBCL patients have different survivals after diagnosis. 40% of patients respond well to current therapy and have prolonged survival, whereas the remainders survive less than 5 years. In this study, we have applied artificial neural network to classify patients with DLBCL on the basis of their gene expression profiles. Finally, we have attempted to extract a number of genes that their differential expression were significant in DLBCL subtypes.
Methods: We studied 40 patients and 4026 genes. In this study, genes were ranked based on their signal to noise (S/N) ratios. After selecting a suitable threshold, some of them whose ratios were less than the threshold were removed. Then we used PCA for more reducing and Perceptron neural network for classification of these patients. We extracted some appropriate genes based on their prediction ability.
Results: We considered various targets for patients classifying. Thus patients were classified based on their 5 years survival with accuracy of 93%, in regard to Alizadeh et al study results with accuracy of 100%, and regarding with their International Prognosis Index (IPI) with accuracy of 89%.
Conclusion: Combination of PCA and S/N ratio is an effective method for the reduction of the dimension and neural network is a robust tool for classification of patients according to their gene expression profile.
Keywords: classification, gene expression, DLBCL, neural network, Perceptron

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