ArticleBMC bioinformatics2022
Predicting circRNA-drug sensitivity associations via graph attention auto-encoder.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 43 citations in OpenAlex.
- Predicting circRNA-Drug Sensitivity Using Integrated Hybrid Graph and Molecular Features.Interdisciplinary sciences, computational life sciences · 2026Article
- AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA-drug sensitivity association prediction.BMC biology · 2026Article
- Predicting circRNA subcellular localization by fusing circRNA sequence and network information.Scientific reports · 2026Article
- CFGSCDSA: Predicting circRNA-drug sensitivity associations based on collaborative feature learning and graph structure learning.PLoS computational biology · 2026Article
- Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction.BMC biology · 2025Article
- DeepHeteroCDA: circRNA-drug sensitivity associations prediction via multi-scale heterogeneous network and graph attention mechanism.Briefings in bioinformatics · 2025Article
- Circular RNA-Drug Association Prediction Based on Multi-Scale Convolutional Neural Networks and Adversarial Autoencoders.International journal of molecular sciences · 2025Article
- DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks.BMC bioinformatics · 2025Article
- Geometry-enhanced graph neural networks accelerate circRNA therapeutic target discovery.Frontiers in genetics · 2025Article
- DMAGCL: A dual-masked adaptive graph contrastive learning framework for predicting circRNA-drug sensitivity.Frontiers in genetics · 2025Article
- SGTCDA: Prediction of circRNA-drug sensitivity associations with interpretable graph transformers and effective assessment.BMC genomics · 2024Article
- CMAGN: circRNA-miRNA association prediction based on graph attention auto-encoder and network consistency projection.BMC bioinformatics · 2024Article
- Predicting the potential associations between circRNA and drug sensitivity using a multisource feature-based approach.Journal of cellular and molecular medicine · 2024Article
- A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations.Scientific reports · 2024Article
- Article
- Prediction of microbe-drug associations based on a modified graph attention variational autoencoder and random forest.Frontiers in microbiology · 2024Article
- AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding.PLoS computational biology · 2024Article
- Inferring circRNA-drug sensitivity associations via dual hierarchical attention networks and multiple kernel fusion.BMC genomics · 2023Article
- MNCLCDA: predicting circRNA-drug sensitivity associations by using mixed neighbourhood information and contrastive learning.BMC medical informatics and decision making · 2023Article
- RDRGSE: A Framework for Noncoding RNA-Drug Resistance Discovery by Incorporating Graph Skeleton Extraction and Attentional Feature Fusion.ACS omega · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundCircular RNAs (circRNAs) play essential roles in cancer development and therapy resistance. Many studies have shown that circRNA is closely related to human health. The expression of circRNAs also affects the sensitivity of cells to drugs, thereby significantly affecting the efficacy of drugs. However, traditional biological experiments are time-consuming and expensive to validate drug-related circRNAs. Therefore, it is an important and urgent task to develop an effective computational method for predicting unknown circRNA-drug associations.
resultsIn this work, we propose a computational framework (GATECDA) based on graph attention auto-encoder to predict circRNA-drug sensitivity associations. In GATECDA, we leverage multiple databases, containing the sequences of host genes of circRNAs, the structure of drugs, and circRNA-drug sensitivity associations. Based on the data, GATECDA employs Graph attention auto-encoder (GATE) to extract the low-dimensional representation of circRNA/drug, effectively retaining critical information in sparse high-dimensional features and realizing the effective fusion of nodes' neighborhood information. Experimental results indicate that GATECDA achieves an average AUC of 89.18% under 10-fold cross-validation. Case studies further show the excellent performance of GATECDA.
conclusionsMany experimental results and case studies show that our proposed GATECDA method can effectively predict the circRNA-drug sensitivity associations.
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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 OpenQuestion graph.