2024-03-28T13:29:00Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/73372023-03-21T09:16:00Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220
Barbero Aparicio, José Antonio
819
500
0000-0002-3269-0806
Cuesta López, Santiago
133
600
0000-0002-7401-3889
García Osorio, César
212
600
0000-0002-1206-1084
Pérez-Rodríguez, Javier
8947c154-0779-4026-afe0-763d5303e772
600
García-Pedrajas, Nicolás
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2023-01-26T12:32:03Z
2023-01-26T12:32:03Z
2022-12
http://hdl.handle.net/10259/7337
10.1186/s12859-022-05129-4
1471-2105
There is evidence that DNA breathing (spontaneous opening of the DNA strands)
plays a relevant role in the interactions of DNA with other molecules, and in particular
in the transcription process. Therefore, having physical models that can predict these
openings is of interest. However, this source of information has not been used before
either in transcription start sites (TSSs) or promoter prediction. In this article, one such
model is used as an additional information source that, when used by a machine learn‑
ing (ML) model, improves the results of current methods for the prediction of TSSs. In
addition, we provide evidence on the validity of the physical model, as it is able by itself
to predict TSSs with high accuracy. This opens an exciting avenue of research at the
intersection of statistical mechanics and ML, where ML models in bioinformatics can be
improved using physical models of DNA as feature extractors.
This work has been supported by the Junta de Andalucia under project UCO1264182 and by the Ministry of Science, Innovation and Universities under project PID2019-109481GB-I00/AEI/q10.13039/501100011033, in both cases co-financed through European Union FEDER funds. José A. Barbero-Aparicio is founded through a predoctoral grant from the University of Burgos.
application/pdf
eng
Springer Nature
BMC Bioinformatics. 2022, V. 23, n. 1, 565
https://doi.org/10.1186/s12859-022-05129-4
info:eu-repo/grantAgreement/Junta de Andalucía//UCO-1264182/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109481GB-I00/ES/NUEVA APROXIMACION A LA CONSTRUCCION DE ENJAMBRES PARA APRENDIZAJE MULTI-ETIQUETA: APLICACION A LA QUEMINFORMATICA Y LA BIOINFORMATICA/
Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
DNA modelling
DNA breathing
Machine learning
TSS prediction
SVM
String kernels
Informática
Computer science
Nonlinear physics opens a new paradigm for accurate transcription start site prediction
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
BMC Bioinformatics
23
1
THUMBNAIL
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oai:riubu.ubu.es:10259/7337
2023-03-21 10:16:00.126
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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