ArticleInterdisciplinary sciences, computational life sciences2021
Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA-lncRNA Interaction Prediction.
Article in Interdisciplinary sciences, computational life 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.
- Application of Artificial Intelligence Technology in Plant MicroRNA Research: Progress, Challenges, and Prospects.International journal of molecular sciences · 2025Review
- Enhancing LncRNA-miRNA interaction prediction with multimodal contrastive representation learning.Briefings in bioinformatics · 2025Article
- Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences.Briefings in bioinformatics · 2024Article
- MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism.Frontiers in genetics · 2024Article
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
- Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments.International journal of molecular sciences · 2023Review
- PmliHFM: Predicting Plant miRNA-lncRNA Interactions with Hybrid Feature Mining Network.Interdisciplinary sciences, computational life sciences · 2023Article
- Artificial intelligence methods enhance the discovery of RNA interactions.Frontiers in molecular biosciences · 2022Review
- Identification of long non-coding RNAs involved in floral scent ofFrontiers in plant science · 2022Article
- Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants.Frontiers in plant science · 2022Article
- LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification.BMC bioinformatics · 2021Article
- Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization.Frontiers in genetics · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are both non-coding RNAs (ncRNAs) and their interactions play important roles in biological processes. Computational methods, such as machine learning and various bioinformatics tools, can predict potential miRNA-lncRNA interactions, which is significant for studying their mechanisms and biological functions. A growing number of RNA interaction predictors for animal have been reported, but they are unreliable for plant due to the differences of ncRNAs in animal and plant. It is urgent to build a reliable plant predictor, especially for cross-species. This paper proposes an ensemble deep learning model based on multi-level information enhancement and greedy fuzzy decision (PmliPEMG) for plant miRNA-lncRNA interaction prediction. The fusion complex features, multi-scale convolutional long short-term memory networks, and attention mechanism are adopted to enhance the sample information at the feature, scale, and model levels, respectively. An ensemble deep learning model is built based on a novel method (greedy fuzzy decision) which greatly improves the efficiency. The multi-level information enhancement and greedy fuzzy decision are verified to have the positive effects on prediction performance. PmliPEMG can be applied to the cross-species prediction. It shows better performance and stronger generalization ability than state-of-the-art predictors and may provide valuable references for related research.
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