ArticleInternational journal of molecular sciences2019
RPITER: A Hierarchical Deep Learning Framework for ncRNA⁻Protein Interaction Prediction.
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 29 papers.
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Who cites it
29 citing papers in PubMed.
- A Deep Learning Framework for Accurate lncRNA-Protein Interaction Prediction via Multi-Kernel Graph Embeddings and Self-Attention.Interdisciplinary sciences, computational life sciences · 2026Article
- CG-Based Stratification of 8-mers Highlights Functional Roles and Phylogenetic Divergence Markers.International journal of molecular sciences · 2025Article
- NPI-HetGNN: A Prediction Model of ncRNA-Protein Interactions Based on Heterogeneous Graph Neural Networks.Interdisciplinary sciences, computational life sciences · 2025Article
- BioLLMNet: enhancing RNA-interaction prediction with a specialized cross-LLM transformation network.Briefings in bioinformatics · 2025Article
- Article
- LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer.Computational and structural biotechnology journal · 2025Article
- A Deep Learning Model to Predict the ncRNA-Protein Interactions Based on Sequences Information Only.Bioinformatics and biology insights · 2025Article
- Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding.BMC genomics · 2024Article
- BioPrediction-RPI: Democratizing the prediction of interaction between non-coding RNA and protein with end-to-end machine learning.Computational and structural biotechnology journal · 2024Article
- Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm.BMC bioinformatics · 2024Article
- A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.Nucleic acids research · 2023Article
- DCiPatho: deep cross-fusion networks for genome scale identification of pathogens.Briefings in bioinformatics · 2023Article
- RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction.Nucleic acids research · 2023Article
- DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design.PLoS computational biology · 2023Article
- PmliHFM: Predicting Plant miRNA-lncRNA Interactions with Hybrid Feature Mining Network.Interdisciplinary sciences, computational life sciences · 2023Article
- Exploring the landscape of tools and resources for the analysis of long non-coding RNAs.Computational and structural biotechnology journal · 2023Review
- A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis.Nature communications · 2022Article
- Artificial intelligence methods enhance the discovery of RNA interactions.Frontiers in molecular biosciences · 2022Review
- 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
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4 authors.
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Abstract
Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implicated in many complex human diseases. Mostly ncRNAs function by interacting with corresponding RNA-binding proteins. The research on ncRNA⁻protein interaction is the key to understanding the function of ncRNA. However, the biological experiment techniques for identifying RNA⁻protein interactions (RPIs) are currently still expensive and time-consuming. Due to the complex molecular mechanism of ncRNA⁻protein interaction and the lack of conservation for ncRNA, especially for long ncRNA (lncRNA), the prediction of ncRNA⁻protein interaction is still a challenge. Deep learning-based models have become the state-of-the-art in a range of biological sequence analysis problems due to their strong power of feature learning. In this study, we proposed a hierarchical deep learning framework RPITER to predict RNA⁻protein interaction. For sequence coding, we improved the conjoint triad feature (CTF) coding method by complementing more primary sequence information and adding sequence structure information. For model design, RPITER employed two basic neural network architectures of convolution neural network (CNN) and stacked auto-encoder (SAE). Comprehensive experiments were performed on five benchmark datasets from PDB and NPInter databases to analyze and compare the performances of different sequence coding methods and prediction models. We found that CNN and SAE deep learning architectures have powerful fitting abilities for the
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