ResMGCN: Residual Message Graph Convolution Network for Fast Biomedical Interactions Discovering

arXiv:2311.07632v2 Announce Type: replace
Abstract: Biomedical information graphs are crucial for interaction discovering of biomedical information in modern age, such as identification of multifarious molecular interactions and drug discovery, which attracts increasing interests in biomedicine, bioinformatics, and human healthcare communities. Nowadays, more and more graph neural networks have been proposed to learn the entities of biomedical information and precisely reveal biomedical molecule interactions with state-of-the-art results. These methods remedy the fading of features from a far distance but suffer from …

abstract age arxiv bioinformatics biomedical biomedicine communities convolution cs.ai cs.lg discovery drug discovery graph graph neural networks graphs healthcare human identification information interactions modern network networks neural networks q-bio.mn residual type