A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data

TL;DR

This paper introduces GEDFN, a novel deep learning model that integrates gene network information for improved disease outcome classification and feature selection from gene expression data, addressing the small sample size and high feature dimensionality challenge.

Contribution

The paper proposes GEDFN, a graph-embedded deep feedforward network that incorporates external gene network data to enhance classification accuracy and feature interpretability in gene expression analysis.

Findings

GEDFN achieves high classification accuracy on breast cancer RNA-seq data.

The method effectively performs feature selection aligned with biological networks.

Simulation and real data validate the robustness of GEDFN.

Abstract

Gene expression data represents a unique challenge in predictive model building, because of the small number of samples $(n)$ compared to the huge amount of features $(p)$. This "$n<<p$" property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating external gene network information could be a potential solution to this issue. Still, the problem is very challenging because (1) there are tens of thousands of features and only hundreds of training samples, (2) the scale-free structure of the gene network is unfriendly to the setup of convolutional neural networks. To address these issues and build a robust classification model, we propose the Graph-Embedded Deep Feedforward Networks (GEDFN), to integrate external relational information of features into the deep neural network architecture. The method is able to achieve sparse connection between network layers to prevent overfitting. To validate the method's capability, we conducted both simulation experiments and a real data analysis using a breast cancer RNA-seq dataset from The Cancer Genome Atlas (TCGA). The resulting high classification accuracy and easily interpretable feature selection results suggest the method is a useful addition to the current classification models and feature selection procedures. The method is available at https://github.com/yunchuankong/NetworkNeuralNetwork.