ArticleMolecules (Basel, Switzerland)2020
LncMirNet: Predicting LncRNA-miRNA Interaction Based on Deep Learning of Ribonucleic Acid Sequences.
Article in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 75 citations in OpenAlex.
- Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction.BMC biology · 2025Article
- Enhancing LncRNA-miRNA interaction prediction with multimodal contrastive representation learning.Briefings in bioinformatics · 2025Article
- lncRNA ACVR2B-AS1 modulates thyroid cancer progression by regulating miR-195-5p.Discover oncology · 2025Article
- Navigating the Multiverse: a Hitchhiker's guide to selecting harmonization methods for multimodal biomedical data.Biology methods & protocols · 2025Review
- SORFPP: Enhancing rich sequence-driven information to identify SEPs based on fused framework on validation datasets.PloS one · 2025Article
- IRGL-RRI: interpretable graph representation learning for plant RNA-RNA interaction discovery.Frontiers in plant science · 2025Article
- Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting.Medical oncology (Northwood, London, England) · 2024Review
- AEmiGAP: AutoEncoder-Based miRNA-Gene Association Prediction Using Deep Learning Method.International journal of molecular sciences · 2024Article
- Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer.Computational and structural biotechnology journal · 2024Article
- Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences.Briefings in bioinformatics · 2024Article
- LPI-SKMSC: Predicting LncRNA-Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering.Interdisciplinary sciences, computational life sciences · 2024Article
- Advances in applications of artificial intelligence algorithms for cancer-related miRNA research.Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2024Review
- Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer.bioRxiv : the preprint server for biology · 2024Article
- Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.Nucleic acids research · 2023Article
- RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction.Nucleic acids research · 2023Article
- Interactome of Long Non-Coding RNAs: Transcriptomic Expression Patterns and Shaping Cancer Cell Phenotypes.International journal of molecular sciences · 2023Review
- PmliHFM: Predicting Plant miRNA-lncRNA Interactions with Hybrid Feature Mining Network.Interdisciplinary sciences, computational life sciences · 2023Article
- BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA-miRNA interaction prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- A Graph Neural Network Approach for the Analysis of siRNA-Target Biological Networks.International journal of molecular sciences · 2022Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long non-coding RNA (LncRNA) and microRNA (miRNA) are both non-coding RNAs that play significant regulatory roles in many life processes. There is cumulating evidence showing that the interaction patterns between lncRNAs and miRNAs are highly related to cancer development, gene regulation, cellular metabolic process, etc. Contemporaneously, with the rapid development of RNA sequence technology, numerous novel lncRNAs and miRNAs have been found, which might help to explore novel regulated patterns. However, the increasing unknown interactions between lncRNAs and miRNAs may hinder finding the novel regulated pattern, and wet experiments to identify the potential interaction are costly and time-consuming. Furthermore, few computational tools are available for predicting lncRNA-miRNA interaction based on a sequential level. In this paper, we propose a hybrid sequence feature-based model, LncMirNet (lncRNA-miRNA interactions network), to predict lncRNA-miRNA interactions via deep convolutional neural networks (CNN). First, four categories of sequence-based features are introduced to encode lncRNA/miRNA sequences including k-mer (k = 1, 2, 3, 4), composition transition distribution (CTD), doc2vec, and graph embedding features. Then, to fit the CNN learning pattern, a histogram-dd method is incorporated to fuse multiple types of features into a matrix. Finally, LncMirNet attained excellent performance in comparison with six other state-of-the-art methods on a real dataset collected from lncRNASNP2 via five-fold cross validation. LncMirNet increased accuracy and area under curve (AUC) by more than 3%, respectively, over that of the other tools, and improved the Matthews correlation coefficient (MCC) by more than 6%. These results show that LncMirNet can obtain high confidence in predicting potential interactions between lncRNAs and miRNAs.
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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.