AIMC Topic: Deep Learning

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Data-driven prediction of future purchase behavior in cross-border e-commerce using sequence modeling with PSO-tuned LSTM.

PloS one
With the rapid advancement of cross-border e-commerce, accurately predicting user purchase behavior has emerged as a critical challenge for enhancing platform operational efficiency and user experience. This study proposes a hybrid deep learning fram...

BioFusionDTI: Assimilating Graph and Sequence Modalities for Generalizable Drug-Target Interaction Prediction.

Journal of chemical information and modeling
Accurate prediction of drug-target interactions (DTIs) is essential for drug discovery and repurposing. Despite recent advances, deep learning models often exhibit limited generalization under realistic cold-start scenarios and suffer from poor inter...

Model-based spatiotemporal synthetic data generation framework and deep-learning reconstruction for real-time MRI oxygen extraction fraction mapping.

Physics in medicine and biology
Synthetic data has emerged as a highly efficient solution to address the scarcity of training data in deep learning-based quantitative magnetic resonance imaging (qMRI) reconstruction. However, current applications of synthetic data predominantly foc...

Can artificial intelligence and face recognition using deep learning detect emotions in children with autism?

PloS one
BACKGROUND/OBJECTIVES: This study aimed to evaluate the performance of deep learning models for recognizing facial expressions of children with autism through face recognition technologies.

ZNGEA: ZINB-NMF Integrated Graph Embedding Autoencoder for Metabolite-Disease Association Identification.

Analytical chemistry
Metabolism, a series of complex and orderly chemical reactions within a biological organism, has a significant impact on sustaining life activities. Disease development is often linked to alterations in the types or levels of metabolites; however, tr...

Volumetric localization microscopy with deep learning.

Nature communications
Super-resolution microscopy, particularly localization-based methods, necessitates careful balancing of optical complexity, computational demands, and user accessibility. Conventional strategies typically adopt either deterministic or learning-based ...

Mammo-AGE: deep learning estimation of breast age from mammograms.

Nature communications
Biological age is an important indicator of organ functions and health. Although mammograms are widely used in breast cancer screening, the potential of mammogram-based biological age predictors remains underexplored. Here, we propose a deep learning...

Deep learning reveals endogenous sterols as allosteric modulators of the GPCR-Gα interface.

eLife
Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for unbiased cavity identification, cavity-specific li...

Automatic detection of urinary stones from non-contrast enhanced computed tomography images.

Urolithiasis
Urinary stones, one of the most common emergency conditions, traverse the ureter, urine flow is obstructed, resulting in hydronephrosis and severe pain. However, vessel wall calcifications or phleboliths are frequently observed in abdominal and pelvi...

Intelligent retinal disease detection using deep learning.

Scientific reports
The rising prevalence of retinal diseases is a significant concern, as certain untreated conditions can lead to severe vision impairment or even blindness. Deep learning algorithms have emerged as a powerful tool for the diagnosis and analysis of med...