A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data

Identifying Cancer Stage-Related Biomarkers for Lung Adenocarcinoma by Integrating Both Node and Edge Features

Background: In order to characterize phenotypes and diseases, genetic factors and their interactions in biological systems must be considered. Although genes or node features are the core units of genetic information, their connections, also known as edge features, are composed of a network of gene interactions. These components are crucial for understanding the molecular basis of disease and phenotype development. Existing research typically utilizes node biomarkers composed of individual genes or proteins for the binary classification of cancer. However, due to significant heterogeneity among patients, these methods cannot adapt to the subtle changes required for precise cancer staging, and relying solely on node biomarkers often leads to poor accuracy in classifying cancer staging. Methods: In this study, a computational framework was developed to diagnose lung adenocarcinoma, integrating node and edge features such as correlation, covariance, and residuals. The proposed method allows for precise diagnosis in the case of a single sample, which can identify the minimum feature set that effectively distinguishes cancer staging. Results: The advantages of the proposed method are: (i) it can diagnose each individual test sample, promoting personalized treatment; (ii) integrating node and edge features can improve diagnostic accuracy, indicating that each type of feature can capture unique aspects of the disease; (iii) it significantly reduces the number of features required to accurately classify the four stages of cancer, thereby achieving optimal cross-validation accuracy. Conclusions: This streamlined and effective feature set highlights the potential of our approach in advancing personalized medicine and improving clinical outcomes for cancer patients.

Bi-level gene selection of cancer by combining clustering and sparse learning

The diagnosis of cancer based on gene expression profile data has attracted extensive attention in the field of biomedical science. This type of data usually has the characteristics of high dimensionality and noise. In this paper, a hybrid gene selection method based on clustering and sparse learning is proposed to choose the key genes with high precision. We first propose a filter method, which combines the k-means clustering algorithm and signal-to-noise ratio ranking method, and then, a weighted gene co-expression network has been applied to the reduced data set to identify modules corresponding to biological pathways. Moreover, we choose the key genes by using group bridge and sparse group lasso as wrapper methods. Finally, we conduct some numerical experiments on six cancer datasets. The numerical results show that our proposed method has achieved good performance in gene selection and cancer classification.

Transformer-Based Multi-Modal Data Fusion Method for COPD Classification and Physiological and Biochemical Indicators Identification

As the number of modalities in biomedical data continues to increase, the significance of multi-modal data becomes evident in capturing complex relationships between biological processes, thereby complementing disease classification. However, the current multi-modal fusion methods for biomedical data require more effective exploitation of intra- and inter-modal interactions, and the application of powerful fusion methods to biomedical data is relatively rare. In this paper, we propose a novel multi-modal data fusion method that addresses these limitations. Our proposed method utilizes a graph neural network and a 3D convolutional network to identify intra-modal relationships. By doing so, we can extract meaningful features from each modality, preserving crucial information. To fuse information from different modalities, we employ the Low-rank Multi-modal Fusion method, which effectively integrates multiple modalities while reducing noise and redundancy. Additionally, our method incorporates the Cross-modal Transformer to automatically learn relationships between different modalities, facilitating enhanced information exchange and representation. We validate the effectiveness of our proposed method using lung CT imaging data and physiological and biochemical data obtained from patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD). Our method demonstrates superior performance compared to various fusion methods and their variants in terms of disease classification accuracy.