ArticleJournal of cellular and molecular medicine2024
LncRNA-miRNA interactions prediction based on meta-path similarity and Gaussian kernel similarity.
Article in Journal of cellular and molecular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Mechanistic insights into HAGLROS-mediated therapy resistance in ovarian cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- AMPred-MFG: Investigating the Mutagenicity of Compounds Using Motif-Based Graph Combined with Molecular Fingerprints and Graph Attention Mechanism.Interdisciplinary sciences, computational life sciences · 2026Article
- MicroRNAs in endometriosis: bioinformatics resources, machine learning strategies, and multi-omics perspectives.Journal of translational medicine · 2026Review
- MV-HAGCN: Prediction of miRNA-Disease Association Based on Multi-View Hybrid Attention Graph Convolutional Network.International journal of molecular sciences · 2026Article
- DeepLMI: deep feature mining with a globally enhanced graph convolutional network for robust lncRNA-miRNA interaction prediction.Bioinformatics (Oxford, England) · 2026Article
- Single-Cell Transcriptomic Analysis of Chemotherapy-Induced Changes in Osteosarcoma With a Pyroptosis-Related Gene-Based Prognostic Model.Journal of cellular and molecular medicine · 2026Article
- ARID3A Dysregulation Drives Colon Cancer Progression and Enhances Responsiveness to Aspirin.Journal of cellular and molecular medicine · 2026Article
- MiRInter-Trans: a transformer-based framework for microRNA interaction prediction.Bioinformatics advances · 2026Article
- DSMR: Dual-Stream Networks with Refinement Module for Unsupervised Multi-modal Image Registration.Interdisciplinary sciences, computational life sciences · 2025Article
- MFH-LPI: based on multi-view similarity networks fusion and hypergraph learning for long non-coding RNA-protein interactions prediction.BMC genomics · 2025Article
- CNV-mediated dysregulation of the ceRNA network mechanism revealed heterogeneity in diffuse and intestinal gastric cancers.Journal of translational medicine · 2025Article
- A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across Infection Types.Journal of cellular and molecular medicine · 2025Article
- Intratumoral Heterogeneity and Immune Microenvironment in Hepatoblastoma Revealed by Single-Cell RNA Sequencing.Journal of cellular and molecular medicine · 2025Article
- Investigating the relevance of nucleotide metabolism in the prognosis of glioblastoma through bioinformatics models.Scientific reports · 2025Article
- E-SegNet: E-Shaped Structure Networks for Accurate 2D and 3D Medical Image Segmentation.Research (Washington, D.C.) · 2025Article
- Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.Journal of cellular and molecular medicine · 2025Review
- Computational strategies in tumor phylogenetics: evaluating multimodal integration and methodological trade-offs across study designs.Bioinformatics advances · 2025Review
- Design and implementation of a radiomic-driven intelligent dental hospital diversion system utilizing multilabel imaging data.Journal of translational medicine · 2024Article
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8 authors.
Funding
Abstract
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two typical types of non-coding RNAs that interact and play important regulatory roles in many animal organisms. Exploring the unknown interactions between lncRNAs and miRNAs contributes to a better understanding of their functional involvement. Currently, studying the interactions between lncRNAs and miRNAs heavily relies on laborious biological experiments. Therefore, it is necessary to design a computational method for predicting lncRNA-miRNA interactions. In this work, we propose a method called MPGK-LMI, which utilizes a graph attention network (GAT) to predict lncRNA-miRNA interactions in animals. First, we construct a meta-path similarity matrix based on known lncRNA-miRNA interaction information. Then, we use GAT to aggregate the constructed meta-path similarity matrix and the computed Gaussian kernel similarity matrix to update the feature matrix with neighbourhood information. Finally, a scoring module is used for prediction. By comparing with three state-of-the-art algorithms, MPGK-LMI achieves the best results in terms of performance, with AUC value of 0.9077, AUPR of 0.9327, ACC of 0.9080, F1-score of 0.9143 and precision of 0.8739. These results validate the effectiveness and reliability of MPGK-LMI. Additionally, we conduct detailed case studies to demonstrate the effectiveness and feasibility of our approach in practical applications. Through these empirical results, we gain deeper insights into the functional roles and mechanisms of lncRNA-miRNA interactions, providing significant breakthroughs and advancements in this field of research. In summary, our method not only outperforms others in terms of performance but also establishes its practicality and reliability in biological research through real-case analysis, offering strong support and guidance for future studies and applications.
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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.