Adaptive hypergraph regularized logistic regression model for bioinformatic selection and classification

The classification of cancer using established biological knowledge has become increasingly prevalent, primarily due to the improved accuracy and enhanced biological interpretability this method offers for classification outcomes. Despite these advances, current cancer classification methods encounter challenges in maintaining the intricate structure of gene networks and leveraging the statistical information embedded within gene data. In this paper, we introduce an adaptive hypergraph regularized logistic regression model that capitalizes on established biological knowledge and statistical information within gene data. Specifically, our model integrates a hypergraph into the objective function, an innovation that preserves the complex gene network structure more effectively. Additionally, we implement adaptive penalties in the penalty term, which facilitates the targeted selection of disease-related genes based on gene weights. To further refine our model, we incorporate constraints on gene pairs with high statistical correlations within the penalty term, thereby minimizing the inclusion of redundant genes. We adopt the block coordinate descent algorithm to address the nonconvexity of our model. Through comparative experimentation with established methodologies on real datasets, our proposed model demonstrates marked improvement in classification accuracy and adept selection of genes pertinent to specific diseases.