ArticleInternational journal of molecular sciences2021
LRGCPND: Predicting Associations between ncRNA and Drug Resistance via Linear Residual Graph Convolution.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Attention-Guided Multi-View Contrastive Learning for Predicting Sparse Drug-Gene Associations.Interdisciplinary sciences, computational life sciences · 2026Article
- MIFNDRA: an innovative knowledge-enhanced multimodal fusion and graph learning framework for predicting drug resistance-related ncRNAs.Briefings in bioinformatics · 2026Article
- MVGAE: A Multi-View Graph Auto-Encoder Model for Drug Prediction of Non-Small Cell Lung Cancer Based on Synthetic Lethality.Current issues in molecular biology · 2026Article
- Multi-view fusion based on graph convolutional network with attention mechanism for predicting miRNA related to drugs.PLoS computational biology · 2025Article
- Disentangled contrastive learning with dynamic intent adaptation for unveiling gene-drug associations.Briefings in bioinformatics · 2025Article
- DMGAT: predicting ncRNA-drug resistance associations based on diffusion map and heterogeneous graph attention network.Briefings in bioinformatics · 2025Article
- Empowering Graph Neural Networks with Block-Based Dual Adaptive Deep Adjustment for Drug Resistance-Related NcRNA Discovery.Journal of chemical information and modeling · 2024Article
- SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.Briefings in bioinformatics · 2024Article
- Non-Coding RNAs and the Development of Chemoresistance to Docetaxel in Prostate Cancer: Regulatory Interactions and Approaches Based on Machine Learning Methods.Life (Basel, Switzerland) · 2023Review
- DNA and RNA Molecules as a Foundation of Therapy Strategies for Treatment of Cardiovascular Diseases.Pharmaceutics · 2023Review
- RDRGSE: A Framework for Noncoding RNA-Drug Resistance Discovery by Incorporating Graph Skeleton Extraction and Attentional Feature Fusion.ACS omega · 2023Article
- Non-Coding RNAs as Novel Regulators of Neuroinflammation in Alzheimer's Disease.Frontiers in immunology · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Accurate inference of the relationship between non-coding RNAs (ncRNAs) and drug resistance is essential for understanding the complicated mechanisms of drug actions and clinical treatment. Traditional biological experiments are time-consuming, laborious, and minor in scale. Although several databases provide relevant resources, computational method for predicting this type of association has not yet been developed. In this paper, we leverage the verified association data of ncRNA and drug resistance to construct a bipartite graph and then develop a linear residual graph convolution approach for predicting associations between non-coding RNA and drug resistance (LRGCPND) without introducing or defining additional data. LRGCPND first aggregates the potential features of neighboring nodes per graph convolutional layer. Next, we transform the information between layers through a linear function. Eventually, LRGCPND unites the embedding representations of each layer to complete the prediction. Results of comparison experiments demonstrate that LRGCPND has more reliable performance than seven other state-of-the-art approaches with an average AUC value of 0.8987. Case studies illustrate that LRGCPND is an effective tool for inferring the associations between ncRNA and drug resistance.
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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 Socratic graph.