Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/117437
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Klimentova, Eva | - |
dc.contributor.author | Polacek, Jakub | - |
dc.contributor.author | Simecek, Petr | - |
dc.contributor.author | Alexiou, Panagiotis | - |
dc.date.accessioned | 2024-01-17T08:11:28Z | - |
dc.date.available | 2024-01-17T08:11:28Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Klimentova, E., Polacek, J., Simecek, P., & Alexiou, P. (2020). PENGUINN: Precise exploration of nuclear G-quadruplexes using interpretable neural networks. Frontiers in Genetics, 11, 568546. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/117437 | - |
dc.description.abstract | G-quadruplexes (G4s) are a class of stable structural nucleic acid secondary structures that are known to play a role in a wide spectrum of genomic functions, such as DNA replication and transcription. The classical understanding of G4 structure points to four variable length guanine strands joined by variable length nucleotide stretches. Experiments using G4 immunoprecipitation and sequencing experiments have produced a high number of highly probable G4 forming genomic sequences. The expense and technical difficulty of experimental techniques highlights the need for computational approaches of G4 identification. Here, we present PENGUINN, a machine learning method based on Convolutional neural networks, that learns the characteristics of G4 sequences and accurately predicts G4s outperforming state-of-the-art methods. We provide both a standalone implementation of the trained model, and a web application that can be used to evaluate sequences for their G4 potential. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Frontiers Media SA | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Bioinformatics | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Web applications | en_GB |
dc.subject | Computational biology | en_GB |
dc.subject | Quadruplex nucleic acids | en_GB |
dc.subject | Genomics -- Case studies | en_GB |
dc.subject | Neural networks (Computer science) | en_GB |
dc.title | PENGUINN : precise exploration of nuclear G-quadruplexes using interpretable neural networks | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.3389/fgene.2020.568546 | - |
dc.publication.title | Frontiers in Genetics | en_GB |
Appears in Collections: | Scholarly Works - FacHScABS |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
PENGUINN.pdf | 358.12 kB | Adobe PDF | View/Open |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.