2024-03-29T01:44:46Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/73372023-03-21T09:16:00Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220
Nonlinear physics opens a new paradigm for accurate transcription start site prediction
Barbero Aparicio, José Antonio
Cuesta López, Santiago
García Osorio, César
Pérez-Rodríguez, Javier
García-Pedrajas, Nicolás
DNA modelling
DNA breathing
Machine learning
TSS prediction
SVM
String kernels
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.
2023-01-26T12:32:03Z
2023-01-26T12:32:03Z
2023-01-26T12:32:03Z
2022-12
info:eu-repo/semantics/article
http://hdl.handle.net/10259/7337
10.1186/s12859-022-05129-4
1471-2105
eng
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/
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Atribución 4.0 Internacional
Springer Nature