2023/04/25 by Laurent Bréhélin, Bréhélin, Laurent
Biochemistry, Genetics and Molecular Biology · #FOS: Biological sciences #Genomics (q-bio.GN) #Genomics and Chromatin Dynamics #Machine Learning in Bioinformatics #RNA and protein synthesis mechanisms
paper · pdf · doi:10.48550/arxiv.2304.12963
openalex publication_date 2023/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.