The development of sensors with high sensitivity, good flexibility, low cost, and capability of detecting multiple inputs is of great significance for wearable electronics. Herein, we report a napkin-based wearable capacitive sensor fabricated by a novel, low-cost, and facile strategy. The capacitive sensor is composed of two pieces of electrode plates manufactured by spontaneous assembly of silver nanowires (NWs) on a polydimethylsiloxane (PDMS)-patterned napkin. The sensor possesses high sensitivity (>7.492 kPa⁻¹), low cost, and capability for simultaneous detection of multiple signals. We demonstrate that the capacitive sensor can be applied to identify a variety of human physiological signals, including finger motions, eye blinking, and minute wrist pulse. More interestingly, the capacitive sensor comfortably attached to the temple can simultaneously monitor eye blinking and blood pulse. The demonstrated sensor shows great prospects in the applications of human–machine interface, prosthetics, home-based healthcare, and flexible touch panels.

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.

The selection of critical features from microarray data as biomarkers holds significant importance in disease diagnosis and drug development. It is essential to reduce the number of biomarkers while maintaining their performance to effectively minimize subsequent validation costs. However, the processing of microarray data often encounters the challenge of the “curse of dimensionality”. Existing feature-selection methods face difficulties in effectively reducing feature dimensionality while ensuring classification accuracy, algorithm efficiency, and optimal search space exploration. This paper proposes a hybrid feature-selection algorithm based on an enhanced version of the Max Relevance and Min Redundancy (mRMR) method, coupled with differential evolution. The proposed method improves the quantization functions of mRMR to accommodate the continuous nature of microarray data attributes, utilizing them as the initial step in feature selection. Subsequently, an enhanced differential evolution algorithm is employed to further filter the features. Two adaptive mechanisms are introduced to enhance early search efficiency and late population diversity, thus reducing the number of features and balancing the algorithm’s exploration and exploitation. The results highlight the improved performance and efficiency of the hybrid algorithm in feature selection for microarray data analysis.

Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification.

Background: In the field of genomics and personalized medicine, it is a key issue to find biomarkers directly related to the diagnosis of specific diseases from high-throughput gene microarray data. Feature selection technology can discover biomarkers with disease classification information. Results: We use support vector machines as classifiers and use the five-fold cross-validation average classification accuracy, recall, precision and F1 score as evaluation metrics to evaluate the identified biomarkers. Experimental results show classification accuracy above 0.93, recall above 0.92, precision above 0.91, and F1 score above 0.94 on eight microarray datasets. Method: This paper proposes a two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization (EF-BDBA), which can effectively reduce the dimension of microarray data and obtain optimal biomarkers. In the first stage, we propose an ensemble filter feature selection method. The method combines an improved fast correlation-based filter algorithm with Fisher score. obviously redundant and irrelevant features can be filtered out to initially reduce the dimensionality of the microarray data. In the second stage, the optimal feature subset is selected using an improved binary differential evolution incorporating an improved binary African vultures optimization algorithm. The African vultures optimization algorithm has excellent global optimization ability. It has not been systematically applied to feature selection problems, especially for gene microarray data. We combine it with a differential evolution algorithm to improve population diversity. Conclusion: Compared with traditional feature selection methods and advanced hybrid methods, the proposed method achieves higher classification accuracy and identifies excellent biomarkers while retaining fewer features. The experimental results demonstrate the effectiveness and advancement of our proposed algorithmic model.