SFWN: A Novel Semi-Supervised Feature Weighted Neural Network for Gene Data Feature Learning and Mining With Graph Modeling

Gene expression data can serve for analyzing the genes with changed expressions, the correlation between genes and the influence of different circumstance on gene activities. However, labeling a large number of gene expression data is laborious and time-consuming. The insufficient labeled data pose a challenge to construct the deep learning model. Currently, some graph neural networks (GNN) based on semi-supervised learning mechanism only focus on the feature space and sample space of gene expression data, possibly affecting the accuracy. This article puts forward a novel semi-supervised graph neural network model (SFWN). Firstly, we use the external knowledge of gene expression data for constructing a feature graph, a similarity kernel, and a sample graph for the first time. Later, a novel semi-supervised learning algorithm (SGA) is proposed to extract the data relationship and obtain the global sample structure better. A graph sparse module (SGCN) is also proposed to process sparse representation with gene expression data classification. To overcome the over smoothing problem, a new feature calculation method based on two spaces is proposed to feature representation analysis and calculation in this model. According to a lot of experiments and ablation studies conducted on several public datasets, SFWN exhibits a better effect and is superior to the state-of-the-art approaches (the accuracy and F1-Score are 0.9993 and 0.9899, respectively). Experimental results showed that the proposed SFWN model has strong gene expression feature learning and representation ability, and may provide a new insight and tool for relevant disease diagnosis and clinic practice.