Results 271 to 280 of about 9,562,225 (293)

Prediction of Structural Noncoding RNAs With RNAz

2007
The function of many noncoding RNAs (ncRNAs) depend on a defined secondary structure. RNAz detects evolutionarily conserved and thermodynamically stable RNA secondary structures in multiple sequence alignments and, thus, efficiently filters for candidate ncRNAs. In this chapter, we provide a step-by-step guide on how to use RNAz.
openaire   +3 more sources

Rich Parameterization Improves RNA Structure Prediction

Journal of Computational Biology, 2011
Abstract Current approaches to RNA structure prediction range from physics-based methods, which rely on thousands of experimentally measured thermodynamic parameters, to machine-learning (ML) techniques. While the methods for parameter estimation are successfully shifting toward ML-based approaches, the model ...
Shay Zakov, Yoav Goldberg, Michael Elhadad, Michal Ziv-Ukelson   +3 more
openaire   +4 more sources

RNA Structure Prediction

Annual Review of Biophysics and Biophysical Chemistry, 1988
D H, Turner, N, Sugimoto, S M, Freier
openaire   +3 more sources

Energy-Directed RNA Structure Prediction

2013
In this chapter we present the classic dynamic programming algorithms for RNA structure prediction by energy minimization, as well as variations of this approach that allow to compute suboptimal foldings, or even the partition function over all possible secondary structures.
openaire   +2 more sources

IDENTIFYING GOOD PREDICTIONS OF RNA SECONDARY STRUCTURE

Biocomputing 2004, 2003
Predicting the secondary structure of RNA molecules from the knowledge of the primary structure (the sequence of bases) is still a challenging task. There are algorithms that provide good results e.g. based on the search for an energetic optimal configuration.
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A phylogenetic approach to RNA structure prediction.

Proceedings. International Conference on Intelligent Systems for Molecular Biology, 2000
Methods based on the Mutual Information statistic (MI methods) predict structure by looking for statistical correlations between sequence positions in a set of aligned sequences. Although MI methods are often quite effective, these methods ignore the underlying phylogenetic relationships of the sequences they analyze.
Viatcheslav R. Akmaev, Scott T. Kelley, Gary D. Stormo   +2 more
openaire   +2 more sources

Modulation of Noncoding RNAs (ncRNAs) and Their Potential Role as Therapeutics

Medical Epigenetics, 2021
Luciano Pirola, Aneta Balcerczyk, Oskar Ciesielski   +2 more
exaly  

UFold: Fast and Accurate RNA Secondary Structure Prediction With Deep Learning

Nucleic Acids Research, 2022
Laiyi Fu, Xiaohui Xie, Qing Nie
exaly  

RNA Structure Prediction

2023
openaire   +1 more source

RNA Secondary Structure Prediction Using Deep Learning With Thermodynamic Integration

Nature Communications, 2021
Kengo Sato, Yasubumi Sakakibara
exaly