MF-DAT: a stock trend prediction of the double-graph attention network based on multisource information fusion
Sign Up to like & getrecommendations! Published in 2024 at "Multimedia Systems"
DOI: 10.1007/s00530-024-01333-9
Abstract: Stock forecasting research, which aims to predict the future price movement of stocks, has been the focus of investors and scholars. This is important for practical applications related to human-centric computing and information sciences. Previous… read more here.
Keywords: graph attention; based multisource; information; relationship ... See more keywords
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
ResGAT: Residual Graph Attention Networks for molecular property prediction
Sign Up to like & getrecommendations! Published in 2024 at "Memetic Computing"
DOI: 10.1007/s12293-024-00423-5
Abstract: Molecular property prediction is an important step in the drug discovery pipeline. Numerous computational methods have been developed to predict a wide range of molecular properties. While recent approaches have shown promising results, no single… read more here.
Keywords: graph attention; attention networks; property prediction; graph ... 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
dMXP: A De Novo Small-Molecule 3D Structure Predictor with Graph Attention Networks
Sign Up to like & getrecommendations! Published in 2024 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.4c00391
Abstract: Generating the three-dimensional (3D) structure of small molecules is crucial in both structure- and ligand-based drug design. Structure-based drug design needs bioactive conformations of compounds for lead identification and optimization. Ligand-based drug design techniques, such… read more here.
Keywords: graph attention; molecule; structure; novo small ... See more keywords
SynthMol: A Drug Safety Prediction Framework Integrating Graph Attention and Molecular Descriptors into Pre-Trained Geometric Models
Sign Up to like & getrecommendations! Published in 2025 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.4c01320
Abstract: Drug safety is affected by multiple molecular properties and safety assessment is critical for clinical application. Evaluating a drug candidate's therapeutic potential is facilitated by machine learning models trained on extensive compound bioactivity data sets,… read more here.
Keywords: graph attention; drug safety; pre trained; drug ... 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
Multi-layer graph attention neural networks for accurate drug-target interaction mapping
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DOI: 10.1038/s41598-024-75742-1
Abstract: In the crucial process of drug discovery and repurposing, precise prediction of drug-target interactions (DTIs) is paramount. This study introduces a novel DTI prediction approach—Multi-Layer Graph Attention Neural Network (MLGANN), through a groundbreaking computational framework… read more here.
Keywords: graph attention; layer graph; drug; drug target ... See more keywords