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...
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...
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 ...
CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection 来自 EconPapers 喜欢 0 阅读量: 64 作者:H Li,Z Han,Y Sun,F Wang,P Hu,Y Gao,X Bai,S Peng,C Ren,X Xu 摘要: Cancer is rarely the straightforward consequence of an abnormality in ...
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 graphs, GNNs lack explainability, which largely limits their adoption in scenarios that demand the transparen...
In this paper, we propose SCARLET (truSt and Credibility bAsed gRaph neuraL nEtwork model using aTtention) to predict likely action of nodes in the spread path. We aggregate trust and credibility features from a node's neighborhood using historical behavioral data and network structure and explain ...
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 ...
- Explainable Graph neural networks for networking - Explainable sequential decision-making - Constraints-based explanations for networking - Visualizations and tools for understanding and interpreting machine learning models in networking - Case studies and real-world applications of explainable and safety bo...
StableGNN is the framework of Graph Neural Network solutions that provide increase of stability to noise data and increase the accuracy for out-of-distribution data. It consists of three parts:graph - load and adjust data model - based of geom-gcn, with ability to include self-superised ...
Graph neural networks (GNNs) are powerful tools for performing data science tasks in various domains. Although we use GNNs in wide application scenarios, it is a laborious task for researchers and practitioners to design/select optimal GNN architectures in diverse graphs. To save human efforts and...