Accurate diagnosis of rolling bearing faults is vital for the safe operation of rotating machinery. However, real-world fault datasets often suffer from severe class imbalance, which hinders the performance of deep learning models. To address this ch...
OBJECTIVE: To develop and validate prediction models for peak oxygen uptake (VO₂peak) in patients with coronary heart disease (CHD) using submaximal cardiopulmonary exercise testing (CPET) indicators and deep learning methods.
Biochemical and biophysical research communications
Oct 4, 2025
The extracellular matrix (ECM) is crucial in tuning cellular behavior, and quantifying cellular mechanical changes in response to ECM stimuli can help reveal the underlying physical mechanisms of cell-ECM interactions for a comprehensive understandin...
Processive endoglucanases, which possess both endo- and exoglucanase activities, are considered highly promising catalysts in cellulose degradation. In this study, we employed multiple deep learning models, including MutCompute, DeepSequence, and ESM...
Cognitive and motor functions require a coordinated communication among brain regions, with the directionality of interactions playing a key role, as the brain relies on functional asymmetries of reciprocal connections. Predictive models based on dee...
Motor imagery-based Brain-Computer Interfaces (BCIs) hold transformative potential for individuals with severe motor impairments, yet their clinical deployment remains constrained by the inherent complexity of electroencephalographic (EEG) signal dec...
Renal cancer is a key reason for cancer-related deaths among males worldwide. Earlier diagnosis of renal cancer is critical since it can considerably increase the chance of survivability. However evaluating the histopathological renal tissue is a ted...
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the gradual decline in cognitive functions, particularly memory and reasoning. Early detection, especially during cognitive impairment (MCI) stage, is crucial for t...
Accurate age estimation is essential for advancing interspecies communication but remains a challenge across non-human species. This study presents the first dataset of domestic feline vocalisations specifically designed for age prediction and introd...
This study introduces a robust and efficient hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BLSTM) networks for the automated detection and classification of cardiac arrhy...
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