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Graph Neural Networks

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Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks.

Briefings in bioinformatics
Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for decipherin...

Implementing the discontinuous-Galerkin finite element method using graph neural networks with application to diffusion equations.

Neural networks : the official journal of the International Neural Network Society
Machine learning (ML) has benefited from both software and hardware advancements, leading to increasing interest in capitalising on ML throughout academia and industry. There have been efforts in the scientific computing community to leverage this de...

On the probability of necessity and sufficiency of explaining Graph Neural Networks: A lower bound optimization approach.

Neural networks : the official journal of the International Neural Network Society
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining appr...

Heterophilous distribution propagation for Graph Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success relies on the homophily assumption that nearby nodes exhibit similar be...

Drug toxicity prediction model based on enhanced graph neural network.

Computers in biology and medicine
Prediction of drug toxicity remains a significant challenge and an essential process in drug discovery. Traditional machine learning algorithms struggle to capture the full scope of molecular structure features, limiting their effectiveness in toxici...

Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network.

BMC bioinformatics
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...

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
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 ...

MDFGNN-SMMA: prediction of potential small molecule-miRNA associations based on multi-source data fusion and graph neural networks.

BMC bioinformatics
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...

User preference interaction fusion and swap attention graph neural network for recommender system.

Neural networks : the official journal of the International Neural Network Society
Recommender systems are widely used in various applications. Knowledge graphs are increasingly used to improve recommendation performance by extracting valuable information from user-item interactions. However, current methods do not effectively use ...

Graph neural networks in histopathology: Emerging trends and future directions.

Medical image analysis
Histopathological analysis of whole slide images (WSIs) has seen a surge in the utilization of deep learning methods, particularly Convolutional Neural Networks (CNNs). However, CNNs often fail to capture the intricate spatial dependencies inherent i...