Recognition of cancer mediating genes using the novel MLP-SDAE model

Abstract

In the present article, we introduce a deep auto-encoder model to have identified some possible genes mediating different cancer. This new version is a regression-primarily based totally fateful version primarily based totally on MLP and Stacked Denoising Auto-Encoder and the model name is MLP-SDAE. This version is educated via way of means of the usage of a Stacked Denoising Auto-Encoder for characteristic selection. Also, there’s a MLP structure for backpropagation. We have also included dropout to prevent over-fitting to improve our proposed model as well as performance. The process entails companies of gene-primarily based totally correlation coefficients and in the end decided on a few feasible genes. A specified comparative examine has been executed with a number of the prevailing deep studying procedures, such as a Recurrent Neural Network (RNN), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Auto-encoder (AE), and Denoising Auto-encoder (DAE). Four microarray gene expression data sets like human leukemia, lung, colon, and breast cancer are 1 Springer Nature 2021 L A T E X template Recognition of cancer mediating genes using the novel MLP-SDAE model successfully applied in this model. The results are verified using the preliminary test, gene expression profile plots, biochemical pathway, t-test, and also identified GO type of the important genes based on p-values.