CAGAT: centrality-adjusted graph attention network for active scientific talent discovery
Sign Up to like & getrecommendations! Published in 2022 at "Personal and Ubiquitous Computing"
DOI: 10.1007/s00779-021-01659-5
Abstract: Scientific talents are the cornerstone of science and technology development. The current method to find out the scientific talent is almost based on the scientists’ achievement, less considering the interrelationships hidden in the objects. In… read more here.
Keywords: centrality adjusted; scientific talent; adjusted graph; attention ... See more keywords
Unsupervised medical images denoising via graph attention dual adversarial network
Sign Up to like & getrecommendations! Published in 2021 at "Applied Intelligence"
DOI: 10.1007/s10489-020-02016-4
Abstract: A lot of natural images denoising methods have been proposed, however, there are dual primary challenges for medical images denoising: 1) paired datasets are scarce and 2) medical images are often three-dimensional. In this paper,we… read more here.
Keywords: network; images denoising; medical images; adversarial network ... See more keywords
HetGAT: a heterogeneous graph attention network for freeway traffic speed prediction
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-020-02807-0
Abstract: As an essential part of the modern intelligent traffic management system, traffic speed prediction is a challenging task. In recent studies, deep neural networks (LSTM and WaveNet) and graph neural networks (GCN and GNN) have… read more here.
Keywords: speed; attention network; graph attention; graph ... See more keywords
Short-term traffic speed forecasting based on graph attention temporal convolutional networks
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.001
Abstract: Abstract Accurate and timely traffic forecasting is significant for intelligent transportation management. However, existing approaches model the temporal and spatial features of traffic flow inadequately. To address these limitations, a novel deep learning traffic forecasting… read more here.
Keywords: traffic; based graph; temporal convolutional; attention temporal ... See more keywords
Graph Attention Network Model with Defined Applicability Domains for Screening PBT Chemicals.
Sign Up to like & getrecommendations! Published in 2022 at "Environmental science & technology"
DOI: 10.1021/acs.est.2c00765
Abstract: In silico models for screening environmentally persistent, bio-accumulative, and toxic (PBT) substances are necessary for sound management of chemicals. Due to the complex structure-activity landscapes (SALs) on the PBT attributes, previous models for screening PBT… read more here.
Keywords: model; graph attention; screening pbt; pbt chemicals ... See more keywords
Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder.
Sign Up to like & getrecommendations! Published in 2022 at "Nature communications"
DOI: 10.1038/s41467-022-29439-6
Abstract: Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use… read more here.
Keywords: spatially resolved; resolved transcriptomics; spatial domains; attention ... See more keywords
ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac350
Abstract: An unsolved challenge in developing molecular representation is determining an optimal method to characterize the molecular structure. Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new graph-attention-based approach,… read more here.
Keywords: graph attention; molecular representation; representation; drug discovery ... See more keywords
AGAT-PPIS: a novel protein-protein interaction site predictor based on augmented graph attention network with initial residual and identity mapping.
Sign Up to like & getrecommendations! Published in 2023 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbad122
Abstract: Identifying protein-protein interaction (PPI) site is an important step in understanding biological activity, apprehending pathological mechanism and designing novel drugs. Developing reliable computational methods for predicting PPI site as screening tools contributes to reduce lots… read more here.
Keywords: protein protein; attention network; graph attention; site ... See more keywords
hpGAT: High-Order Proximity Informed Graph Attention Network
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2938039
Abstract: Graph neural networks (GNNs) have recently made remarkable breakthroughs in the paradigm of learning with graph-structured data. However, most existing GNNs limit the receptive field of the node on each layer to its connected (one-hop)… read more here.
Keywords: order proximity; graph attention; high order;
FPGAN: An FPGA Accelerator for Graph Attention Networks With Software and Hardware Co-Optimization
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3023946
Abstract: The Graph Attention Networks (GATs) exhibit outstanding performance in multiple authoritative node classification benchmark tests (including transductive and inductive). The purpose of this research is to implement an FPGA-based accelerator called FPGAN for graph attention… read more here.
Keywords: graph attention; software hardware; hardware optimization; attention networks ... See more keywords
Graph Attention Networks With Local Structure Awareness for Knowledge Graph Completion
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3044343
Abstract: Graph neural networks have been proven to be very effective for representation learning of knowledge graphs. Recent methods such as SACN and CompGCN, have achieved the most advanced results in knowledge graph completion. However, previous… read more here.
Keywords: knowledge graph; graph attention; graph completion; graph ... See more keywords