ArticleBMC bioinformatics2020
Dual graph convolutional neural network for predicting chemical networks.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- Sort & Slice: a simple and superior alternative to hash-based folding for extended-connectivity fingerprints.Journal of cheminformatics · 2024Article
- EGGNet, a Generalizable Geometric Deep Learning Framework for Protein Complex Pose Scoring.ACS omega · 2024Article
- Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance.Journal of chemical information and modeling · 2023Article
- Identifying Candidate Gene-Disease Associations via Graph Neural Networks.Entropy (Basel, Switzerland) · 2023Article
- AcneGrader: An ensemble pruning of the deep learning base models to grade acne.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2022Article
- SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug-drug interactions.Bioinformatics (Oxford, England) · 2022Article
- Chemical property prediction under experimental biases.Scientific reports · 2022Article
- Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion.Briefings in bioinformatics · 2022Article
- Graph Neural Networks and Their Current Applications in Bioinformatics.Frontiers in genetics · 2021Article
- Identification of potential biomarkers and available drugs for oral squamous cell carcinoma.Translational cancer research · 2021Article
- Prediction of the Drug-Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks.Frontiers in pharmacology · 2021Article
- CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph.BMC bioinformatics · 2020Article
- Graph convolutional networks: a comprehensive review.Computational social networks · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of available data has enabled applications of computational approaches such as statistical modeling and machine learning method. Both a set of chemical interactions and chemical compound structures are represented as graphs, and various graph-based approaches including graph convolutional neural networks have been successfully applied to chemical network prediction. However, there was no efficient method that can consider the two different types of graphs in an end-to-end manner.
resultsWe give a new formulation of the chemical network prediction problem as a link prediction problem in a graph of graphs (GoG) which can represent the hierarchical structure consisting of compound graphs and an inter-compound graph. We propose a new graph convolutional neural network architecture called dual graph convolutional network that learns compound representations from both the compound graphs and the inter-compound network in an end-to-end manner.
conclusionsExperiments using four chemical networks with different sparsity levels and degree distributions shows that our dual graph convolution approach achieves high prediction performance in relatively dense networks, while the performance becomes inferior on extremely-sparse networks.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.