Data science and molecular biology: prediction and mechanistic explanation

Abstract

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium-The Cancer Genome Atlas-which stands at the forefront in integrating data science and molecular biology. The result will be that biologists

Machine learning in oncology: a review

“…(In this discussion, we will consider ML algorithms applications in the clinics or in the lab and not the more abstract knowledge-generating tasks such as the mapping of the Cancer Genome Atlas described previously. This latter question is less a methodological one and more of an epistemological one, having to do with the logic of induction and scientific discovery: see, for instance, López-Rubio and Ratti [ 10 ].)…”

Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in Artificial Intelligence

“…They might assume more exploratory functions though, such as in delimiting the search domain for new particles (Boge and Grünke, in press). In the biomedical sciences, DNNs are considered useful to the extent that they provide information on genomic patterns and correlations that would otherwise remain hidden, and thereby support more traditional mechanistic models, but they are also being used in fully data-driven approaches that accept a trade-off between predictive power and model transparency (López-Rubio & Ratti, 2021;Facchini & Termine, in preparation). There is no a priori way of determining a genuine set of possible roles that DNNs might play in these and other sciences, but the development and implementation of models that would directly contribute to scientific explanation and theory building does not seem to be among these roles.…”

The Explanatory Role of Machine Learning in Molecular Biology

“…Philosophers of science and scientists have taken up the central question of this debate by addressing the impact that the introduction of AI and machine learning is likely to have on the general character of scientific research (e.g. Pietsch, 2015;Canali, 2016;Coveney et al, 2016;Boon, 2020;Creel, 2020;Ourmazd, 2020;Boge and Poznic, 2021;López-Rubio and Ratti, 2021;Boge et al, 2022;Krenn et al, 2022;Duede, 2023;Andrews, 2023). The areas of genetics and molecular biology, which over the last few decades have become highly "data-centric" (Leonelli, 2016), seem particular prone to making the shift from a theory-or hypothesis-driven mode towards purely data-driven modes of research.…”