Convolutional Neural Network Approach to Lung Cancer Classification Integrating Protein Interaction Network and Gene Expression Profiles

Abstract

Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks to 'omics' data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a convolutional neural network (CNN) approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. Data and CNN code can be downloaded from the link: https://sites.google.com/site/nacherlab/analysis