A Novel Biomarker Selection Method Combining Graph Neural Network and Gene Relationships Applied to Microarray Data

Background: The discovery of critical biomarkers is significant for clinical diagnosis, drug research and development. Researchers usually obtain biomarkers from microarray data, which comes from the dimensional curse. Feature selection in machine learning is usually used to solve this problem. However, most methods do not fully consider feature dependence, especially the real pathway relationship of genes. Results: Experimental results show that the proposed method is superior to classical algorithms and advanced methods in feature number and accuracy, and the selected features have more significance. Method: This paper proposes a feature selection method based on a graph neural network. A graph structure and real feature dependence combined with Pearson correlation coefficient is used in our method to construct graph structure model, the information propagation and aggregation method of graph neural network is used to characterize node information, and the spectral clustering method is used to filter redundant features. Then, the feature ranking aggregation model using eight feature evaluation methods acts on each clustering sub-cluster for different feature selection. Conclusion: The proposed method can effectively remove redundant features. The algorithm’s output has high stability and classification accuracy, which can potentially select potential biomarkers.