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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.

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Primer on Modelling Approaches for Omics Data

  • 章节

  • 发表日期：

    2026年

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  • Guillem A. Santamaria,
  • João Miranda,
  • Margarida Carrolo,
  • Sara Silva,
  • Francisco Rodrigues Pinto

摘要

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.