DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac109
Abstract: Drug-target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery and drug design. However, due to the large size of the chemical and genomic spaces and the complex interactions between drugs and… read more here.
Keywords: heterogeneous graph; graph; prediction; drug ... See more keywords
Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction
Sign Up to like & getrecommendations! Published in 2023 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac578
Abstract: Drug-target interaction (DTI) prediction is an essential step in drug repositioning. A few graph neural network (GNN)-based methods have been proposed for DTI prediction using heterogeneous biological data. However, existing GNN-based methods only aggregate information… read more here.
Keywords: network; heterogeneous graph; prediction; drug ... See more keywords
Heterogeneous graph embedding model for predicting interactions between TF and target gene.
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac148
Abstract: MOTIVATION Identifying the target genes of transcription factors (TFs) is of great significance for biomedical researches. However, using biological experiments to identify TF-target gene interactions is still time consuming, expensive and limited to small scale.… read more here.
Keywords: heterogeneous graph; target gene; model; graph embedding ... See more keywords
Personalized Scientific Paper Recommendation Based on Heterogeneous Graph Representation
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2923293
Abstract: The accelerating rate of scientific publications makes it extremely difficult for researchers to find out the relevant papers and related works. Recommender systems that aim at solving the information overload problem have attracted lots of… read more here.
Keywords: paper recommendation; paper; graph representation; heterogeneous graph ... See more keywords
QoS Prediction of Web Services Based on a Two-Level Heterogeneous Graph Attention Network
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DOI: 10.1109/access.2021.3138127
Abstract: Quality of Service (QoS) prediction for Web services is a hot research topic in the field of services computing. Recently, representation learning of heterogeneous networks has attracted much attention, and specifically the relationship between users… read more here.
Keywords: network; heterogeneous graph; qos prediction; level ... See more keywords
Siamese Network Based Multi-Scale Self-Supervised Heterogeneous Graph Representation Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3187088
Abstract: Owing to label-free modeling of complex heterogeneity, self-supervised heterogeneous graph representation learning (SS-HGRL) has been widely studied in recent years. The goal of SS-HGRL is to design an unsupervised learning framework to represent complicated heterogeneous… read more here.
Keywords: heterogeneous graph; representation learning; supervised heterogeneous; self supervised ... See more keywords
MSHGANMDA: Meta-Subgraphs Heterogeneous Graph Attention Network for miRNA-Disease Association Prediction.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE journal of biomedical and health informatics"
DOI: 10.1109/jbhi.2022.3186534
Abstract: MicroRNAs (miRNAs) influence several biological processes involved in human disease. Biological experiments for verifying the association between miRNA and disease are always costly in terms of both money and time. Although numerous biological experiments have… read more here.
Keywords: heterogeneous graph; disease; association; attention ... See more keywords
Predicting CircRNA-Disease associations via feature convolution learning with heterogeneous graph attention network.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE journal of biomedical and health informatics"
DOI: 10.1109/jbhi.2023.3260863
Abstract: Exploring the relationship between circular RNA (circRNA) and disease is beneficial for revealing the mechanisms of disease pathogenesis. However, a blind search for all possible associations between circRNAs and diseases through biological experiments is time-consuming.… read more here.
Keywords: network; heterogeneous graph; circrna disease; disease ... See more keywords
Automated Configuration of Heterogeneous Graph Neural Networks With a Semantic Math Parser for IoT Systems
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3204889
Abstract: Efficient training of deep learning models from time series data for Internet of Things (IoT) systems requires a good understanding of the domain, particularly if the training is automated for large-scale applications. Heterogeneous graph neural… read more here.
Keywords: neural networks; heterogeneous graph; graph neural; automated configuration ... See more keywords
A Hierarchical Heterogeneous Graph for Unsupervised SAR Image Change Detection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3224454
Abstract: This letter presents a novel graph-driven synthetic aperture radar (SAR) image change detection approach. A hierarchical heterogeneous graph (HHG) is proposed, combining two distinct graphs: a weighted graph based on adjacency of superpixels of an… read more here.
Keywords: change detection; heterogeneous graph; sar image; graph ... See more keywords
An Improved Heterogeneous Graph Convolutional Network for Inter-Relational Medicine Representation Learning
Sign Up to like & getrecommendations! Published in 2023 at "IEEE MultiMedia"
DOI: 10.1109/mmul.2022.3200205
Abstract: Medicine representation learning, which aims at uncovering hidden medicine relationships has emerged as a significant technique to imitate a doctor’s cognitive reasoning process. The majority of present research focuses on the intuitive relationships between medication… read more here.
Keywords: graph convolutional; network; heterogeneous graph; representation learning ... See more keywords