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.
