ArticleBMC medical informatics and decision making2023
MNCLCDA: predicting circRNA-drug sensitivity associations by using mixed neighbourhood information and contrastive learning.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- PMPIHGLL: predicting metabolite-protein interactions using dual hypergraph convolutional networks and large language models.Briefings in bioinformatics · 2026Article
- 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
- DeepLMI: deep feature mining with a globally enhanced graph convolutional network for robust lncRNA-miRNA interaction prediction.Bioinformatics (Oxford, England) · 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
- Circular RNA-Drug Association Prediction Based on Multi-Scale Convolutional Neural Networks and Adversarial Autoencoders.International journal of molecular sciences · 2025Article
- SGTCDA: Prediction of circRNA-drug sensitivity associations with interpretable graph transformers and effective assessment.BMC genomics · 2024Article
- Predicting the potential associations between circRNA and drug sensitivity using a multisource feature-based approach.Journal of cellular and molecular medicine · 2024Article
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundcircRNAs play an important role in drug resistance and cancer development. Recently, many studies have shown that the expressions of circRNAs in human cells can affect the sensitivity of cells to therapeutic drugs, thus significantly influencing the therapeutic effects of these drugs. Traditional biomedical experiments required to verify this sensitivity relationship are not only time-consuming but also expensive. Hence, the development of an efficient computational approach that can accurately predict the novel associations between drug sensitivities and circRNAs is a crucial and pressing need.
methodsIn this research, we present a novel computational framework called MNCLCDA, which aims to predict the potential associations between drug sensitivities and circRNAs to assist with medical research. First, MNCLCDA quantifies the similarity between the given drug and circRNA using drug structure information, circRNA gene sequence information, and GIP kernel information. Due to the existence of noise in similarity information, we employ a preprocessing approach based on random walk with restart for similarity networks to efficiently capture the useful features of circRNAs and drugs. Second, we use a mixed neighbourhood graph convolutional network to obtain the neighbourhood information of nodes. Then, a graph-based contrastive learning method is used to enhance the robustness of the model, and finally, a double Laplace-regularized least-squares method is used to predict potential circRNA-drug associations through the kernel matrices in the circRNA and drug spaces.
resultsNumerous experimental results show that MNCLCDA outperforms six other advanced methods. In addition, the excellent performance of our proposed model in case studies illustrates that MNCLCDA also has the ability to predict the associations between drug sensitivity and circRNA in practical situations.
conclusionsAfter a large number of experiments, it is illustrated that MNCLCDA is an efficient tool for predicting the potential associations between drug sensitivities and circRNAs, thereby can provide some guidance for clinical trials.
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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.