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

arXiv: https://arxiv.org/abs/1801.06202 · HTML: https://mlsanity.com/paper/1801.06202/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2018.

## Built on by (most cited first, 8 of 8)

- [A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction](https://mlsanity.com/paper/2201.10353/) (2022)
- [Hadamard product in deep learning: Introduction, Advances and Challenges](https://mlsanity.com/paper/2504.13112/) (2025)
- [Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review](https://mlsanity.com/paper/2301.12222/) (2023)
- [Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning](https://mlsanity.com/paper/2505.24155/) (2025)
- [Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping](https://mlsanity.com/paper/2404.17886/) (2024)
- [MOTGNN: Interpretable Graph Neural Networks for Multi-Omics Disease Classification](https://mlsanity.com/paper/2508.07465/) (2025)
- [TF-DWGNet: A Directed Weighted Graph Neural Network with Tensor Fusion for Multi-Omics Cancer Subtype Classification](https://mlsanity.com/paper/2509.16301/) (2025)
- [engGNN: A Dual-Graph Neural Network for Omics-Based Disease Classification and Feature Selection](https://mlsanity.com/paper/2601.14536/) (2026)

## Abstract (arXiv)

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.