Título Nonlinear physics opens a new paradigm for accurate transcription start site prediction
Autores Barbero-Aparicio J.A. , Cuesta-Lopez S. , García-Osorio C.I. , PÉREZ RODRÍGUEZ, JAVIER, García-Pedrajas N.
Publicación externa No
Medio BMC Bioinformatics
Alcance Article
Naturaleza Científica
Cuartil JCR 2
Cuartil SJR 1
Impacto JCR 3.00000
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145346819&doi=10.1186%2fs12859-022-05129-4&partnerID=40&md5=e9d8e285d65ceec8777de3351dceb7f0
Fecha de publicacion 30/12/2022
ISI 000906191700001
Scopus Id 2-s2.0-85145346819
DOI 10.1186/s12859-022-05129-4
Abstract 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 learning (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. © 2022, The Author(s).
Palabras clave Forecasting; Machine learning; Statistical mechanics; DNA breathing; DNA modeling; Machine learning models; Machine-learning; Nonlinear physics; Physical modelling; String Kernel; SVM; Transcription s
Miembros de la Universidad Loyola

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