AIMC Topic: Graph Neural Networks

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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...
Models, Molecular Graph Neural Networks Algorithms Protein Conformation Binding Sites Protein Binding Protein Interaction Mapping Neural Networks, Computer Computational Biology Proteins
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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...
Protein Interaction Maps Humans Proteomics Neural Networks, Computer Mutation Breast Neoplasms Algorithms Neoplasm Proteins Graph Neural Networks Biomarkers, Tumor Female
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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...
Algorithms Graph Neural Networks Humans Neural Networks, Computer
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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...
Water Pollutants, Chemical Water Quality Biological Oxygen Demand Analysis Graph Neural Networks Industrial Waste Waste Disposal, Fluid Environmental Monitoring Neural Networks, Computer Wastewater
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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...
Algorithms Imaging, Three-Dimensional Graph Neural Networks Humans Neural Networks, Computer
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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...
Algorithms Drug Discovery Computational Biology Humans Neural Networks, Computer Graph Neural Networks Drug Combinations Drug-Related Side Effects and Adverse Reactions
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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...
Algorithms Graph Neural Networks Humans Neural Networks, Computer
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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...
MicroRNAs Humans Deep Learning Small Molecule Libraries Graph Neural Networks Computational Biology Neural Networks, Computer
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A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jan 10, 2025
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug ...
Neural Networks, Computer Graph Neural Networks HIV Infections HIV-1 Treatment Outcome Drug Resistance, Viral Anti-Retroviral Agents Humans Anti-HIV Agents
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IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network.

Biomolecules Jan 10, 2025
Interleukin-6 (IL-6) is a potent glycoprotein that plays a crucial role in regulating innate and adaptive immunity, as well as metabolism. The expression and release of IL-6 are closely correlated with the severity of various diseases. IL-6-inducing ...
Amino Acid Sequence Interleukin-6 Humans Graph Neural Networks Peptides Neural Networks, Computer
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