Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "ACS Omega"
DOI: 10.1021/acsomega.2c02765
Abstract: Lattice vibrational frequencies are related to many important materials properties such as thermal and electrical conductivity as well as superconductivity. However, computational calculation of vibrational frequencies using density functional theory methods is computationally too demanding… read more here.
Keywords: deep graph; vibrational frequencies; lattice vibrational; graph neural ... See more keywords
AFSE: towards improving model generalization of deep graph learning of ligand bioactivities targeting GPCR proteins.
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac077
Abstract: Ligand molecules naturally constitute a graph structure. Recently, many excellent deep graph learning (DGL) methods have been proposed and used to model ligand bioactivities, which is critical for the virtual screening of drug hits from… read more here.
Keywords: graph learning; generalization; ligand; model ... See more keywords
GraphPLBR: Protein-ligand Binding Residue Prediction with Deep Graph Convolution Network.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE/ACM transactions on computational biology and bioinformatics"
DOI: 10.1109/tcbb.2023.3239983
Abstract: The intermolecular interactions between proteins and ligands occur through site-specific amino acid residues in the proteins, and the identification of these key residues plays a critical role in both interpreting protein function and facilitating drug… read more here.
Keywords: deep graph; ligand binding; prediction; binding residues ... See more keywords
Explaining Deep Graph Networks via Input Perturbation.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3165618
Abstract: Deep graph networks (DGNs) are a family of machine learning models for structured data which are finding heavy application in life sciences (drug repurposing, molecular property predictions) and on social network data (recommendation systems). The… read more here.
Keywords: deep graph; via input; networks via; explaining deep ... See more keywords
Deep Graph Metric Learning for Weakly Supervised Person Re-Identification.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2021.3084613
Abstract: In conventional person re-identification (re-id), the images used for model training in the training probe set and training gallery set are all assumed to be instance-level samples. This labeling across multiple non-overlapping camera views from… read more here.
Keywords: person; video; deep graph; supervised person ... See more keywords
Self-taught hashing using deep graph embedding for large-scale image retrieval
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Electronic Imaging"
DOI: 10.1117/1.jei.29.3.033014
Abstract: Abstract. Existing deep hashing algorithms fail to achieve satisfactory results from unseen data owing to the out-of-sample problem. Graph-embedding-based hashing methods alleviate this by learning the distance between samples. However, they focus on the first-order… read more here.
Keywords: deep graph; self taught; using deep; image ... See more keywords
A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Advanced Transportation"
DOI: 10.1155/2021/6638130
Abstract: Due to the strong propagation causality of delays between airports, this paper proposes a delay prediction model based on a deep graph neural network to study delay prediction from the perspective of an airport network.… read more here.
Keywords: delay prediction; delay; deep graph; network ... See more keywords
A deep graph convolutional neural network architecture for graph classification
Sign Up to like & getrecommendations! Published in 2023 at "PLOS ONE"
DOI: 10.1371/journal.pone.0279604
Abstract: Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more… read more here.
Keywords: neural network; graph convolutional; deep graph; graph ... See more keywords
Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Computational Neuroscience"
DOI: 10.3389/fncom.2022.964686
Abstract: Heart disease is an emerging health issue in the medical field, according to WHO every year around 10 billion people are affected with heart abnormalities. Arteries in the heart generate oxygenated blood to all body… read more here.
Keywords: network; deep graph; internet things; heart ... See more keywords