ArticleCancers2021
A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions.Biomedical engineering online · 2025Review
- H2GnnDTI: hierarchical heterogeneous graph neural networks for drug-target interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning.BMC medical informatics and decision making · 2024Article
- Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining.BMC bioinformatics · 2023Article
- SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features.BMC bioinformatics · 2023Article
- MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug-target interaction.BMC bioinformatics · 2023Article
- Leveraging pre-trained language models for mining microbiome-disease relationships.BMC bioinformatics · 2023Article
- DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.BMC bioinformatics · 2023Article
- GraphCPIs: A novel graph-based computational model for potential compound-protein interactions.Molecular therapy. Nucleic acids · 2023Article
- Drug repurposing and prediction of multiple interaction types via graph embedding.BMC bioinformatics · 2023Article
- Automatic disease prediction from human gut metagenomic data using boosting GraphSAGE.BMC bioinformatics · 2023Article
- pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model.BMC bioinformatics · 2023Article
- Ion-pumping microbial rhodopsin protein classification by machine learning approach.BMC bioinformatics · 2023Article
- Article
- RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction.BMC bioinformatics · 2022Article
- Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier.BMC bioinformatics · 2022Article
- Prediction of drug-target interactions through multi-task learning.Scientific reports · 2022Article
- iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network.PLoS computational biology · 2022Article
- A brief review of protein-ligand interaction prediction.Computational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.
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What Socratic holds
Registered trials
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 Socratic graph.