XGDP represents drugs with molecular graphs, which naturally preserve the structural information of molecules and a Graph Neural Network module is applied to learn the latent features of molecules. Gene expression data from cancer cell lines are incorporated and processed by a Convolutional Neural ...
Here, we present a new framework (CGMega) for dissecting cancer gene module with explainable graph attention. First, we constructed a multi-omics representation graph in which nodes were genes and edges were defined as protein–protein interactions (PPIs) between genes. Node features are the conca...
& Sun, Y. CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection. Zenodo https://zenodo.org/records/10086978 (2024). Download references Acknowledgements This work was supported by the Beijing Natural Science Foundation (http://kw.beijing.gov....
Graph Neural Networks (GNNs) are a popular family of models for learning with graph-structured data (e.g. social networks). GNNs play an important role in the field of Computational Chemistry, where the graphs involved represent molecules. For instance, GNNs are sometimes used to estimate th...
2.3.1. Explainable graph neural network Given a well-trained fGF model for predicting urban functions U, GNNExplainer is utilized to identify the connected subgraphs GSi with the relevant morphological features FSi that are key for predicting the urban function ui. GNNExplainer is a model-agnostic...
In the EKIN model, an entity driven consumer intent graph is built for every user, from this graph we exploit two kinds of information. Such a graph neural network is built with multihop propagation to learn the entities and relationships of consumers and information is distilled to infer the...
The decision-making process of the model is demonstrated by highlighting certain internal states of a graph neural network (GNN). The proposed system is built on top of a GraphVQA framework that implements various GNN-based models for VQA trained on the GQA dataset. The authors evaluated their...
A PyTorch implementation of "Towards Self-Explainable Graph Neural Network" (CIKM 2021).[paper] Abstract Graph Neural Networks (GNNs), which generalize the deep neural networks to graph-structured data, have achieved great success in modeling graphs. However, as an extension of deep learning for ...
Neural Network explic-itly designed for smart home HAR. Our results on two public datasetsshow that this approach provides better explanations than state-of-the-art methods while also slightly improving the recognition rate.Keywords: Human Activity Recognition · Graph Neural Networks ·Smart Homes ...
Most graph neural networks (GNNs) suffer from the over-smoothing problem which limits further improvement of performance. Hence, many studies have decoupled the GNN into two atomic operations, the propagation (P) operation and the transformation (T) operation to propose a paradigm named decoupled gr...