Kinases play a critical role in regulating fundamental cellular processes, including metabolism, signal transduction, and cell growth, primarily through phosphorylation. The dysregulation of kinase activity is implicated in various diseases, highlighting the urgent need for robust and interpretable methodologies to profile this activity. Current approaches frequently depend on overly complex or limited datasets, lack generalizability, or fail to provide meaningful biological insights into the mechanisms governing kinase activity. To address these challenges, we developed an explainable deep learning framework that leverages mass spectrometry-based phosphoproteomics data to profile kinase activity effectively. Our study systematically evaluated deep neural networks (DNNs) and convolutional neural networks (CNNs), incorporating a diverse set of feature inputs, including phosphorylation sites and kinase-substrate relationships. A notable finding was that a three-layer CNN, optimized through rigorous feature selection techniques, demonstrated superior performance, achieving substantial improvements in prediction accuracy and stability when compared to established methods such as kinase-substrate enrichment analysis (KSEA) and the kinase activity ranking pipeline (KARP). We integrated Shapley additive explanations (SHAP) values to enhance interpretability, illuminating biologically significant phosphorylation sites. For example, PAK2-related phosphorylation sites associated with the progression of colon adenocarcinoma and CAMK2D sites integral to adrenergic signaling were identified, thereby effectively linking computational predictions to established molecular pathways. This research illustrates the potential of explainable artificial intelligence in advancing kinase activity profiling by providing accurate and interpretable predictions. Our framework is valuable for elucidating disease mechanisms and identifying therapeutic targets, facilitating broader applications in precision medicine.

By predicting the air pollutant concentration, people can take precautions to avoid overexposure to air pollutants. Consequently, accurate$\mathbf{PM}_{2.5}$prediction becomes more important. In this paper, we propose a$\mathbf{PM}_{2.5}$prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. Our$\mathbf{PM}_{2.5}$prediction system is composed of four parts: data collection, data preprocessing, prediction model construction, and Line platform. To assess the performance of the model prediction, the daily average error and the hourly average accuracy for the duration of a week are calculated. The experimental results show that LSTM based on K- means has the best performance among all methods. Therefore, LSTM based on K-means is chosen to provide real-time$\mathbf{PM}_{2.5}$prediction through the Linebot.

The progress of industrial development naturally leads to the demand for more electrical power. Unfortunately, due to the fear of the safety of nuclear power plants, many countries have relied on thermal power plants, which will cause more air pollutants during the process of coal burning. This phenomenon as well as increased vehicle emissions around us, have constituted the primary factors of serious air pollution. Inhaling too much particulate air pollution may lead to respiratory diseases and even death, especially PM$_{2.5}$. By predicting the air pollutant concentration, people can take precautions to avoid overexposure to air pollutants. Consequently, accurate PM$_{2.5}$prediction becomes more important. In this study, we propose a PM$_{2.5}$prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. Autoencoder and Linear interpolation are adopted for solving the missing value problem. Spearman’s correlation coefficient is used to identify the most relevant features for PM$_{2.5}$. Two prediction models (i.e., LSTM and LSTM based on K-means) are implemented which predict PM$_{2.5}$value for each Airbox device. To assess the performance of the model prediction, the daily average error and the hourly average accuracy for the duration of a week are calculated. The experimental results show that LSTM based on K-means has the best performance among all methods. Therefore, LSTM based on K-means is chosen to provide real-time PM$_{2.5}$prediction through the Linebot.

Recently, fractal analyses have been applied successfully for the image compression, texture analysis and texture image segmentation. The fractal dimension could be used to quantify the texture information. Several methods including box-counting, fractal Brownian motion, and iterative function system etc. can be used to estimate fractal dimension. In this study, the differences of gray value of neighboring pixels are used to estimate the fractal dimension of an ultrasound image of breast lesion by using the fractal Brownian motion. Further, a computer-aided diagnosis system based on the fractal analysis is proposed to classify the breast lesions into two classes benign and malignant. In order to improve the classification performances, the ultrasound image are pre-processed by using morphology operations and histogram equalization. Finally, k-means classification method is used to classify benign tumors from malignant ones. Experimental results will exhibit and evaluate the accuracy rate of the proposed method.