ArticleComputational and structural biotechnology journal2020
Learning distributed representations of RNA and protein sequences and its application for predicting lncRNA-protein interactions.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Article
- MiRInter-Trans: a transformer-based framework for microRNA interaction prediction.Bioinformatics advances · 2026Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- Predict SARS-CoV-2 genome interactions based on RNA language models.Biosafety and health · 2025Article
- Review
- Article
- LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer.Computational and structural biotechnology journal · 2025Article
- A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology.Genes · 2024Review
- CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning.Scientific reports · 2024Article
- Protein feature engineering framework for AMPylation site prediction.Scientific reports · 2024Article
- Precise prediction of phase-separation key residues by machine learning.Nature communications · 2024Article
- Multiple sequence alignment-based RNA language model and its application to structural inference.Nucleic acids research · 2024Article
- LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model.Frontiers in genetics · 2023Article
- Learning to discover medicines.International journal of data science and analytics · 2022Review
- A miRNA Target Prediction Model Based on Distributed Representation Learning and Deep Learning.Computational and mathematical methods in medicine · 2022Article
- SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information.Frontiers in genetics · 2022Article
- DESSO-DB: A web database for sequence and shape motif analyses and identification.Computational and structural biotechnology journal · 2022Article
- Artificial intelligence methods enhance the discovery of RNA interactions.Frontiers in molecular biosciences · 2022Review
- LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification.BMC bioinformatics · 2021Article
- nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.Briefings in bioinformatics · 2021Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The long noncoding RNAs (lncRNAs) are ubiquitous in organisms and play crucial role in a variety of biological processes and complex diseases. Emerging evidences suggest that lncRNAs interact with corresponding proteins to perform their regulatory functions. Therefore, identifying interacting lncRNA-protein pairs is the first step in understanding the function and mechanism of lncRNA. Since it is time-consuming and expensive to determine lncRNA-protein interactions by high-throughput experiments, more robust and accurate computational methods need to be developed. In this study, we developed a new sequence distributed representation learning based method for potential lncRNA-Protein Interactions Prediction, named LPI-Pred, which is inspired by the similarity between natural language and biological sequences. More specifically, lncRNA and protein sequences were divided into
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