2022
Barbero-Aparicio, José Antonio; Cuesta-Lopez, Santiago; García-Osorio, César Ignacio; Pérez-Rodríguez, Javier; García-Pedrajas, Nicolás
Nonlinear physics opens a new paradigm for accurate transcription start site prediction Journal Article
In: BMC Bioinformatics, vol. 23, no. 565, 2022, ISSN: 1471-2105.
Abstract | Links | BibTeX | Tags: DNA breathing, DNA modelling, Machine learning, String kernels, SVM, TSS prediction
@article{Barbero-Aparicio2022,
title = {Nonlinear physics opens a new paradigm for accurate transcription start site prediction},
author = {José Antonio Barbero-Aparicio and Santiago Cuesta-Lopez and César Ignacio García-Osorio and Javier Pérez-Rodríguez and Nicolás García-Pedrajas},
editor = {José Manuel Benítez},
url = {https://doi.org/10.1186/s12859-022-05129-4},
doi = {10.1186/s12859-022-05129-4},
issn = {1471-2105},
year = {2022},
date = {2022-12-30},
urldate = {2022-12-30},
journal = {BMC Bioinformatics},
volume = {23},
number = {565},
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.},
keywords = {DNA breathing, DNA modelling, Machine learning, String kernels, SVM, TSS prediction},
pubstate = {published},
tppubtype = {article}
}
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.