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   Articles with "heterogeneous graph" as a keyword



Crowdsourced bug report severity prediction based on text and image understanding via heterogeneous graph convolutional networks

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recommendations! Published in 2024 at "Journal of Software: Evolution and Process"

DOI: 10.1002/smr.2705

Abstract: In the process of crowdsourced testing, massive bug reports are submitted. Among them, the severity level of the bug report is an important indicator for traigers of crowdsourced platforms to arrange the order of reports… read more here.

Keywords: heterogeneous graph; report; severity; bug report ... See more keywords

DialGNN: Heterogeneous Graph Neural Networks for Dialogue Classification

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recommendations! Published in 2024 at "Neural Processing Letters"

DOI: 10.1007/s11063-024-11595-z

Abstract: Dialogue systems have attracted growing research interests due to its widespread applications in various domains. However, most research work focus on sentence-level intent recognition to interpret user utterances in dialogue systems, while the comprehension of… read more here.

Keywords: heterogeneous graph; classification; dialgnn heterogeneous; graph neural ... See more keywords

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction

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recommendations! Published in 2024 at "Journal of Chemical Information and Modeling"

DOI: 10.1021/acs.jcim.4c00003

Abstract: Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a… read more here.

Keywords: heterogeneous graph; attention; drug; drug synergy ... See more keywords

A Multisource Transformer-Guided Graph Representation Learning Framework for circRNA-Disease Association Prediction

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recommendations! Published in 2025 at "ACS Omega"

DOI: 10.1021/acsomega.5c06830

Abstract: Circular RNAs (circRNAs) possess structural stability and tissue-specific expression patterns, making them potential disease biomarkers. Exploring the associations between circRNAs and diseases is crucial for early diagnosis and targeted treatment. However, due to the complexity… read more here.

Keywords: heterogeneous graph; disease; circrna disease; representation ... See more keywords

Heterogeneous graph neural network-based prediction of immune-related adverse events.

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recommendations! Published in 2025 at "Scientific reports"

DOI: 10.1038/s41598-025-30266-0

Abstract: Immune-related adverse events (irAEs) are common and potentially fatal adverse events. However, predicting irAEs based on clinical medication regimens and basic patient information remains a significant clinical challenge. This study aims to develop a prediction… read more here.

Keywords: heterogeneous graph; adverse events; related adverse; graph neural ... See more keywords

Next location prediction using heterogeneous graph-based fusion network with physical and social awareness

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recommendations! Published in 2024 at "International Journal of Geographical Information Science"

DOI: 10.1080/13658816.2024.2375725

Abstract: Abstract Location prediction based on social media information is highly valuable in human mobility research and has multiple real-life applications. However, existing research methods often ignore social influences, largely ignoring implicit information regarding interactions between… read more here.

Keywords: heterogeneous graph; location prediction; social awareness; physical social ... See more keywords

DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph

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recommendations! 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

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recommendations! 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

Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network

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recommendations! Published in 2024 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbae538

Abstract: Abstract The integration of single-cell RNA sequencing (scRNA-seq) data from multiple experimental batches enables more comprehensive characterizations of cell states. Given that existing methods disregard the structural information between cells and genes, we proposed a… read more here.

Keywords: heterogeneous graph; seq data; graph neural; neural network ... See more keywords

Heterogeneous graph contrastive learning with gradient balance for drug repositioning

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recommendations! Published in 2024 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbae650

Abstract: Abstract Drug repositioning, which involves identifying new therapeutic indications for approved drugs, is pivotal in accelerating drug discovery. Recently, to mitigate the effect of label sparsity on inferring potential drug–disease associations (DDAs), graph contrastive learning… read more here.

Keywords: heterogeneous graph; drug repositioning; graph contrastive; gradient balance ... See more keywords

Heterogeneous graph contrastive learning for integration and alignment of spatial transcriptomics data

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recommendations! Published in 2025 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbaf497

Abstract: Abstract Spatial transcriptomics (ST) technology enables the simultaneous capture of gene expression profile and spatial information within 2D tissue slices. However, conventional analyses that process each individual slice independently often overlook shared features across multiple… read more here.

Keywords: heterogeneous graph; integration; spatial transcriptomics; integration alignment ... See more keywords