MOTIVATION: The accurate classification of phage proteins is critical for advancing bacteriophage research. Despite the proliferation of machine learning approaches in this domain, the persistent issue of data imbalance continues to hinder performance, particularly for rare protein sequences. Previous attempts to address this by re-weighting minority classes have faced limitations due to insufficient feature extraction capabilities. RESULTS: In this paper, we introduce EAGP, a novel approach that integrates a generative model-functionally equivalent to a WGAN yet tailored for one-dimensional data-with the Evolutionary Scale Modeling (ESM) protein large language model for robust feature extraction. EAGP exhibits exceptional performance in binary classification and protein function annotation tasks. Crucially, our method not only improves overall classification efficacy but also significantly alleviates the performance degradation typically observed in minority classes. AVAILABILITY AND IMPLEMENTATION: The data and code underlying this article are available in GitHub at https://github.com/Innerly/EAGP and have been archived on Zenodo at https://doi.org/10.5281/zenodo.19928069.

This study presents systematic design guidelines for AI-powered clinical decision support systems (AI-CDSS) based on a comprehensive literature review and theme analysis. The proposed guidelines are divided into two parts, comprising 25 items: “How—design methods” and “What—design content and forms.” The usability of these guidelines is demonstrated through an AI-CDSS design practice for stroke diagnosis and thrombolytic risk assessment in a Chinese clinical setting. The empirical case study provides practical suggestions for similar AI-CDSS designs that require fast decision-making. Challenges concerning system transparency or explainability and the impact of bias on system output are considered key issues in designing an effective and satisfying AI-CDSS. The potential usefulness of AI-CDSS in clinical decision-making scenarios has been indicated by numerous studies, with efficient design guidelines still needed to address these challenges.

This paper proposes an energy-efficient intelligent pulmonary auscultation system for post COVID-19 era wearable monitoring. This system consists of a tightly coupled two-stage hybrid neural network (TC-TSHNN) model and a corresponding multi-task training paradigm to improve prediction accuracy and generalization ability based on the fact that the number of COVID-19 patients is far less than that of normal people. At the first stage, two-category coarse classification is performed to identify normal and abnormal lung sounds. If the lung sound is abnormal, the second stage would be triggered to perform a four-category fine-grained classification. Besides, discrete wavelet transform is utilized for feature extraction, denoising and data reduction. In addition, advanced lightweight convolutional neural networks are used to reduce the model’s computation and improve the model’s performance. The hybrid network model can achieve 92% computation reduction and energy saving compared with a direct four-category classification when the input lung sound is normal, which is the majority of cases. Experiment results with inter-patient classification on the COVID-19 lung sound dataset from Tongji Hospital in Wuhan City and the ICBHI’17 dataset show that the proposed TC-TSHNN model can significantly reduce power consumption while maintaining competitive performance against the state-of-the-art work.