ArticleInternational journal of molecular sciences2019
BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- MFH-LPI: based on multi-view similarity networks fusion and hypergraph learning for long non-coding RNA-protein interactions prediction.BMC genomics · 2025Article
- A Deep Learning Model to Predict the ncRNA-Protein Interactions Based on Sequences Information Only.Bioinformatics and biology insights · 2025Article
- HormoNet: a deep learning approach for hormone-drug interaction prediction.BMC bioinformatics · 2024Article
- Special Protein or RNA Molecules Computational Identification.International journal of molecular sciences · 2023Article
- Exploring the landscape of tools and resources for the analysis of long non-coding RNAs.Computational and structural biotechnology journal · 2023Review
- ncRPI-LGAT: Prediction of ncRNA-protein interactions with line graph attention network framework.Computational and structural biotechnology journal · 2023Article
- Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants.Frontiers in plant science · 2022Article
- Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions.International journal of molecular sciences · 2021Review
- AptaNet as a deep learning approach for aptamer-protein interaction prediction.Scientific reports · 2021Article
- Recent advances on the machine learning methods in predicting ncRNA-protein interactions.Molecular genetics and genomics : MGG · 2021Review
- DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms.BMC bioinformatics · 2021Article
- Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives.Non-coding RNA · 2020Review
- MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.Journal of translational medicine · 2019Article
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Authors and funding
5 authors.
Funding
Abstract
The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.
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