Advancing regulatory genomics with machine learning - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Preprints, Working Papers, ... Year : 2023

Advancing regulatory genomics with machine learning


In recent years, several machine learning approaches have been proposed to predict gene expression and epigenetic signals from the DNA sequence alone. These models are often used to deduce, and, to some extent, assess putative new biological insights about gene regulation, and they have led to very interesting advances in regulatory genomics. This article reviews a selection of these methods, ranging from linear models to random forests, kernel methods, and more advanced deep learning models. Specifically, we detail the different techniques and strategies that can be used to extract new gene-regulation hypotheses from these models. Furthermore, because these putative insights need to be validated with wet-lab experiments, we emphasize that it is important to have a measure of confidence associated with the extracted hypotheses. We review the procedures that have been proposed to measure this confidence for the different types of machine learning models, and we discuss the fact that they do not provide the same kind of information.
Fichier principal
Vignette du fichier
review.pdf (292.02 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

lirmm-04271442 , version 1 (23-11-2023)



Laurent Brehelin. Advancing regulatory genomics with machine learning. 2023. ⟨lirmm-04271442⟩
8 View
5 Download



Gmail Facebook X LinkedIn More