Traditional hematoxylin and eosin staining in formalin-fixed paraffin-embedded sections, while essential for diagnostic pathology, is time-consuming, labor intensive, and prone to artifacts that can obscure critical histological details. Label-free u...
Vehicle classification is a core task in intelligent transportation systems, where high demands are placed on both computational efficiency and generalization ability in practical applications. Existing deep learning models often struggle to meet the...
Generative deep learning models, such as those used for music generation, can produce a wide variety of results based on perturbations of random points in their latent space. User preferences can be incorporated in the generative process by replacing...
Environmental pollution (Barking, Essex : 1987)
Nov 20, 2025
Nanoplastics are emerging environmental contaminants that increasingly threaten soil ecosystems, yet their effects on microbial behavior remain poorly understood. This is mainly due to the lack of experimental tools capable of directly observing micr...
Journal of chemical information and modeling
Nov 20, 2025
Accurate prediction of compound-protein interactions (CPIs) is critical for drug discovery, but existing data sets often suffer from biases that hinder model generalization. Here, we first highlighted that over-represented molecular scaffolds and imb...
Inferring gene regulatory networks (GRNs) is essential for understanding biological regulation. Although numerous deep learning approaches have been developed for GRN inference, most require large amounts of labeled data. We present Meta-TGLink, a st...
Animals can provide meaningful context for human single-cell data. To transfer information between species, we propose a deep learning approach that pre-trains a conditional variational autoencoder on animal data and transfers its final encoder layer...
BACKGROUND: Advancements in single-cell RNA sequencing have enabled the analysis of millions of cells, but integrating such data across samples and methods while mitigating batch effects remains challenging. Deep learning approaches address this by l...
Longitudinal microbiome data provide a unique opportunity to explore dynamic interactions between microbial communities and disease progression. However, these data are often characterized by missing values, sparse signals, and limited interpretabili...
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