ArticleMolecules (Basel, Switzerland)2018
Feature-Based and String-Based Models for Predicting RNA-Protein Interaction.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Identification of all-against-all protein-protein interactions based on deep hash learning.BMC bioinformatics · 2022Article
- PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences.Life (Basel, Switzerland) · 2022Article
- CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach.PLoS computational biology · 2022Article
- Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners.Biochemical Society transactions · 2020Review
- Identifying Cancer-Specific circRNA-RBP Binding Sites Based on Deep Learning.Molecules (Basel, Switzerland) · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI). In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included much more available information about the RNA-protein pairs. In the second approach, we apply search algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences), and structure information (protein and RNA secondary structures). This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed approaches, including comparative results against leading state-of-the-art methods.
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Registered trials
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