The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted fr...
Graph Neural Network (GNN) and neural network, an end-to-end graph embedding technique based on Graph Signal Processing (GSP), which aggregates the topological information of the neighborhoods of each node in a graph, has attracted wide attention. However, most of the existing GNN models are ...
Multilabel aerial image classification with a concept attention graph neural network IEEE Trans. Geosci. Remote Sens., 60 (2022), pp. 1-12, 10.1109/TGRS.2020.3041461 Google Scholar Lin et al., 2017 Lin, Z., Feng, M., Santos, C.N.d., Yu, M., Xiang, B., Zhou, B., Bengio, Y....
The process begins with an initial graph-encoded material serving as the input. Following this, multiple Augmented Graph Attention (AGAT) layers, each containing 64 neurons, and a DGN (Dynamic Graph Network) are utilized. There is a skip connection from the output of the l-th AGAT layer to...
Research Progress of Graph Neural Network in Knowledge Graph Construction and Application As an effective representation of knowledge, knowledge graph network can be used to represent rich factual information between different categories and bec... XU Xinran,T Wang,LU Cai - 《Journal of Frontiers of...
Secondly, in order to more accurately capture the dependencies between data, for feature extraction of log dependency graphs, Variational Auto Encoder (VAE) is used to capture the potential features of the graph structure, and graph attention is introduced to focus on key nodes in the graph. ...
graph and defines topological potential attraction between nodes and communities,using the incremental comparing with previous time to update the current community structure.The experiment on real network data proved that the proposed algorithm could be more effectively and timely to discover meaningful ...
Besides, Molormer uses lightweight-based attention mechanism and self-attention distilling to process spatially the encoded molecular graph, which not only retains the multi-headed attention mechanism but also reduces the computational and storage costs. Finally, we use the siamese network architecture ...
et al. Graph attention networks. Preprint at arXiv:1710.10903 (2017). Keskin, O., Ma, B. & Nussinov, R. Hot regions in protein–protein interactions: the organization and contribution of structurally conserved hot spot residues. J. Mol. Biol. 345, 1281 (2005). Article CAS PubMed Google...
Except for the single-pose feedforward neural network model, which was outperformed by RF-Score-VS v2, our models outperformed all third-party scoring functions. All multi-pose trained models outperformed the third-party scoring functions; the wide-and-deep neural network achieved the lowest multi...