2026-06-17T23:31:49Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/73372023-03-21T09:16:00Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

00925njm 22002777a 4500 dc Barbero Aparicio, José Antonio author Cuesta López, Santiago author García Osorio, César author Pérez-Rodríguez, Javier author García-Pedrajas, Nicolás author 2022-12 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. http://hdl.handle.net/10259/7337 10.1186/s12859-022-05129-4 1471-2105 DNA modelling DNA breathing Machine learning TSS prediction SVM String kernels Nonlinear physics opens a new paradigm for accurate transcription start site prediction