Omics data allow us to study complex biological systems. Mathematical models can help us understand these systems and extract more knowledge from the data. In this chapter, we introduce three types of mathematical models that can be used to potentiate the interpretation of omics datasets: statistical, machine learning and mechanistic models. We delve into the characteristics of these modelling approaches, the essential stages involved in their development and their potential applications to omics datasets.
Santamaria et al. (Thu,) studied this question.