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

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 pc a 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.