AIMC Topic: Graph Neural Networks

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UNAGI: Unified neighbor-aware graph neural network for multi-view clustering.

Neural networks : the official journal of the International Neural Network Society Jan 23, 2025
Multi-view graph refining-based clustering (MGRC) methods aim to facilitate the clustering of data via Graph Neural Networks (GNNs) by learning optimal graphs that reflect the underlying topology of the data. However, current MGRC approaches are limi...
Algorithms Cluster Analysis Humans Graph Neural Networks Neural Networks, Computer
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GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural Networks.

International journal of molecular sciences Jan 23, 2025
Phosphorylation is one of the most common protein post-translational modifications. The identification of phosphorylation sites serves as the cornerstone for protein-phosphorylation-related research. This paper proposes a protein-phosphorylation site...
Protein Processing, Post-Translational Neural Networks, Computer Algorithms Phosphorylation Amino Acid Sequence Proteins Computational Biology Graph Neural Networks Software
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MVGNN-PPIS: A novel multi-view graph neural network for protein-protein interaction sites prediction based on Alphafold3-predicted structures and transfer learning.

International journal of biological macromolecules Jan 21, 2025
Protein-protein interactions (PPI) are crucial for understanding numerous biological processes and pathogenic mechanisms. Identifying interaction sites is essential for biomedical research and targeted drug development. Compared to experimental metho...
Algorithms Protein Interaction Mapping Protein Binding Graph Neural Networks Computational Biology Protein Conformation Neural Networks, Computer Proteins Binding Sites Models, Molecular
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A graph neural network approach for hierarchical mapping of breast cancer protein communities.

BMC bioinformatics Jan 21, 2025
BACKGROUND: Comprehensively mapping the hierarchical structure of breast cancer protein communities and identifying potential biomarkers from them is a promising way for breast cancer research. Existing approaches are subjective and fail to take info...
Proteomics Neoplasm Proteins Graph Neural Networks Mutation Biomarkers, Tumor Protein Interaction Maps Female Breast Neoplasms Neural Networks, Computer Algorithms Humans
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A Novel session-based recommendation system using capsule graph neural network.

Neural networks : the official journal of the International Neural Network Society Jan 18, 2025
Session-based recommendation systems (SBRS) are essential for enhancing the customer experience, improving sales and loyalty, and providing the possibility to discover products in dynamic and real-world scenarios without needing user history. Despite...
Neural Networks, Computer Algorithms Humans Graph Neural Networks
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Optimizing papermaking wastewater treatment by predicting effluent quality with node-level capsule graph neural networks.

Environmental monitoring and assessment Jan 18, 2025
Papermaking wastewater consists of a sizable amount of industrial wastewater; hence, real-time access to precise and trustworthy effluent indices is crucial. Because wastewater treatment processes are complicated, nonlinear, and time-varying, it is e...
Environmental Monitoring Water Pollutants, Chemical Wastewater Waste Disposal, Fluid Biological Oxygen Demand Analysis Graph Neural Networks Water Quality Industrial Waste Neural Networks, Computer
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SIRE: Scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks.

Medical image analysis Jan 15, 2025
The orientation of a blood vessel as visualized in 3D medical images is an important descriptor of its geometry that can be used for centerline extraction and subsequent segmentation, labeling, and visualization. Blood vessels appear at multiple scal...
Imaging, Three-Dimensional Neural Networks, Computer Graph Neural Networks Algorithms Humans
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Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network.

BMC bioinformatics Jan 15, 2025
In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are crucial in treatment for many diseases. However, the toxic side effects of drug combinations are probably...
Drug-Related Side Effects and Adverse Reactions Graph Neural Networks Neural Networks, Computer Algorithms Humans Computational Biology Drug Discovery Drug Combinations
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A universal strategy for smoothing deceleration in deep graph neural networks.

Neural networks : the official journal of the International Neural Network Society Jan 13, 2025
Graph neural networks (GNNs) have shown great promise in modeling graph-structured data, but the over-smoothing problem restricts their effectiveness in deep layers. Two key weaknesses of existing research on deep GNN models are: (1) ignoring the ben...
Humans Graph Neural Networks Neural Networks, Computer Algorithms
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MDFGNN-SMMA: prediction of potential small molecule-miRNA associations based on multi-source data fusion and graph neural networks.

BMC bioinformatics Jan 13, 2025
BACKGROUND: MicroRNAs (miRNAs) are pivotal in the initiation and progression of complex human diseases and have been identified as targets for small molecule (SM) drugs. However, the expensive and time-intensive characteristics of conventional experi...
Humans Neural Networks, Computer MicroRNAs Small Molecule Libraries Graph Neural Networks Deep Learning Computational Biology
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