ArticleBMC bioinformatics2010
A predictive model for secondary RNA structure using graph theory and a neural network.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Graph-CRISPR: a gene editing efficiency prediction model based on graph neural network with integrated sequence and secondary structure feature extraction.Briefings in bioinformatics · 2025Article
- Advances and opportunities in RNA structure experimental determination and computational modeling.Nature methods · 2022Review
- Review of machine learning methods for RNA secondary structure prediction.PLoS computational biology · 2021Review
- A Polymer Physics Framework for the Entropy of Arbitrary Pseudoknots.Biophysical journal · 2019Article
- Using sequence signatures and kink-turn motifs in knowledge-based statistical potentials for RNA structure prediction.Nucleic acids research · 2017Article
- Predicting Large RNA-Like Topologies by a Knowledge-Based Clustering Approach.Journal of molecular biology · 2016Article
- Graph-based sampling for approximating global helical topologies of RNA.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- RNA graph partitioning for the discovery of RNA modularity: a novel application of graph partition algorithm to biology.PloS one · 2014Article
- Network Theory Tools for RNA Modeling.WSEAS transactions on mathematics · 2013Article
- Quantitative design of regulatory elements based on high-precision strength prediction using artificial neural network.PloS one · 2013Article
- Article
- Classifying multigraph models of secondary RNA structure using graph-theoretic descriptors.ISRN bioinformatics · 2012Article
- Proceedings of the 2011 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference. Introduction.BMC bioinformatics · 2011Article
- Computational approaches to RNA structure prediction, analysis, and design.Current opinion in structural biology · 2011Review
- RAG: an update to the RNA-As-Graphs resource.BMC bioinformatics · 2011Article
- Proceedings of the 2010 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference.BMC bioinformatics · 2010Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDetermining the secondary structure of RNA from the primary structure is a challenging computational problem. A number of algorithms have been developed to predict the secondary structure from the primary structure. It is agreed that there is still room for improvement in each of these approaches. In this work we build a predictive model for secondary RNA structure using a graph-theoretic tree representation of secondary RNA structure. We model the bonding of two RNA secondary structures to form a larger secondary structure with a graph operation we call merge. We consider all combinatorial possibilities using all possible tree inputs, both those that are RNA-like in structure and those that are not. The resulting data from each tree merge operation is represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not, based on the merge data vector. The network estimates the probability of a tree being RNA-like.
resultsThe network correctly assigned a high probability of RNA-likeness to trees previously identified as RNA-like and a low probability of RNA-likeness to those classified as not RNA-like. We then used the neural network to predict the RNA-likeness of the unclassified trees.
conclusionsThere are a number of secondary RNA structure prediction algorithms available online. These programs are based on finding the secondary structure with the lowest total free energy. In this work, we create a predictive tool for secondary RNA structures using graph-theoretic values as input for a neural network. The use of a graph operation to theoretically describe the bonding of secondary RNA is novel and is an entirely different approach to the prediction of secondary RNA structures. Our method correctly predicted trees to be RNA-like or not RNA-like for all known cases. In addition, our results convey a measure of likelihood that a tree is RNA-like or not RNA-like. Given that the majority of secondary RNA folding algorithms return more than one possible outcome, our method provides a means of determining the best or most likely structures among all of the possible outcomes.
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