BACKGROUND: Inferring Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology. Most existing methods fail to consider the skewed degree distribution of genes, complicating the application of directed graph ...
BACKGROUND: Drug repositioning offers a promising avenue for accelerating drug development and reducing costs. Recently, computational repositioning approaches have gained attraction for identifying potential drug-disease associations (DDAs). Biologi...
PURPOSE: Machine learning is a powerful tool to develop algorithms for clinical diagnosis. However, standard machine learning algorithms are not perfectly suited for clinical data since the data are interconnected and may contain time series. As show...
Dementia typically results from damage to neural pathways and the consequent degeneration of neuronal connections. Graph neural networks (GNNs) have been widely employed to model complex brain networks. However, leveraging the complementary temporal,...
Journal of chemical information and modeling
Jun 30, 2025
This work presents a crystal structure prediction framework that employs a structural search using a derivative-free optimization method, with a supervised Graph Neural Network (GNN) model as the energy evaluator. We address the limitations of existi...
Electrocardiogram (ECG) signals play a critical role in diagnosing cardiovascular diseases (CVDs), yet automated ECG classification remains challenging due to inter-patient variability, signal noise, and heart rhythm complexity. To address these chal...
This paper presents a novel framework for detecting and predicting abnormal traffic events on highways. Current traffic monitoring systems often rely on single data sources, which limits their detection accuracy and robustness in complex environments...
Drug-target interaction (DTI) is paramount in drug discovery and repurposing, which involves screening for effective candidate drugs by targeting specific proteins. Existing methods often focus on one or two representations of drugs or targets, and l...
Neural networks : the official journal of the International Neural Network Society
Jun 21, 2025
Heterogeneous Graph Neural Networks (HGNNs) are a powerful tool for modeling data with diverse node and edge types, found in applications like social networks, recommendation systems, and knowledge graphs, including tasks such as node classification,...
Journal of chemical information and modeling
Jun 5, 2025
The forecasting of drug-target interactions (DTIs) is a crucial element in the domain of drug repositioning. Current methodologies, primarily based on dual-branch architectures or graph neural networks (GNNs), typically model binary associations─spec...
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