Identification of Cancer Mediating Biomarkers using Stacked Denoising Autoencoder Model - An Application on Human Lung Data

Abstract

In this work, we form stacked denoising auto encoder model which is recognized few feasible genes mediating human lung adenocarcinoma. At first we have trained the data for feature selection by using Stacked Denoising Auto-encoder (SDAE) and for backpropagation we have used Multilayer Perceptron (MLP) procedure. We said this model is MLP-SDAE. The process include classification of genes according to correlation coefficient value and select few feasible genes. The superiority of the method has been established some present gene selection procedures like Support Vector Machine (SVM), Significance Analysis of Microarry (SAM), Bayesian Regularization (BR), Neighborhood Analysis (NA), and Gaussian Mixture Model (GMM). The MLP-SDAE model has been effectively used to one human lung microarray gene expression data. The result are appropriately verify by preliminary analysis, t-test, and gene expression profile plots. In this method, we have established more number of true positive genes then another present procedures.