As deep learning continues to advance in medical analysis, the increasing complexity of models, particularly Convolutional Neural Networks (CNNs), presents significant challenges related to interpretability, computational costs, and real-world applic...
Journal of chemical information and modeling
Jun 13, 2025
Protein solubility plays a critical role in determining its biological function, such as enabling proper protein delivery and ensuring that proteins remain soluble during cellular processes or therapeutic applications. Accurate prediction of protein ...
Large-scale quantitative studies have identified significant genetic associations for various neurological disorders. Expression quantitative trait locus (eQTL) studies have shown the effect of single-nucleotide polymorphisms (SNPs) on the differenti...
The purpose of this study is to realize the automatic identification and classification of fouls in football matches and improve the overall identification accuracy. Therefore, a Deep Learning-Based Saliency Prediction Model (DLSPM) is proposed. DLSP...
PURPOSE: To enhance glioma segmentation, a 3D-MRI intelligent glioma segmentation method based on deep learning is introduced. This method offers significant guidance for medical diagnosis, grading, and treatment strategy selection.
Approaches studying the dynamics of resting-state functional magnetic resonance imaging (rs-fMRI) activity often focus on time-resolved functional connectivity (tr-FC). While many tr-FC approaches have been proposed, most are linear approaches, e.g. ...
OBJECTIVES: To develop and validate a deep learning model based on three-dimensional features (DL_3D) for distinguishing lung adenocarcinoma (LUAD) from tuberculoma (TBM).
PURPOSE OF REVIEW: Muscle-invasive bladder cancer (MIBC) represents an aggressive malignancy with significant morbidity and mortality. Recent advances in artificial intelligence (AI) offer promising opportunities to enhance patient care across the en...
BACKGROUND: Conventional magnetic resonance imaging (MRI) protocols for lower back pain require multiple sequences and long acquisition times, challenging healthcare systems amid rising demand for lumbar spine imaging.
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