Mechanistic models versus machine learning, a fight worth fighting for the biological community?

Abstract

Ninety per cent of the world's data have been generated in the last 5 years (Machine learning: the power and promise of computers that learn by example Report no. DES4702. Issued April 2017. Royal Society). A small fraction of these data is collected with the aim of validating specific hypotheses. These studies are led by the development of mechanistic models focused on the causality of input-output relationships. However, the vast majority is aimed at supporting statistical or correlation studies that bypass the need for causality and focus exclusively on prediction. Along these lines, there has been a vast increase in the use of machine learning models, in particular in the biomedical and clinical sciences, to try and keep pace with the rate of data generation. Recent successes now beg the question of whether mechanistic models are still relevant in this area. Said otherwise, why should we try to understand the mechanisms of disease progression when we can use machine learning tools to directly predict disease outcome?

Keywords: machine learning; mechanistic modelling; quantitative biology.

Figure 1.

The inputs and outputs from machine learning and mechanistic modelling approaches, and the potential for synergy between the two.