Can Omics Biology Go Subjective because of Artificial Intelligence? A Comment on "Challenges and Opportunities for Bayesian Statistics in Proteomics" by Crook et al.

In their recent review ( 2022, 21 (4), 849-864), Crook et al. diligently discuss the basics (and less basics) of Bayesian modeling, survey its various applications to proteomics, and highlight its potential for the improvement of computational proteomic tools. Despite its interest and comprehensiveness on these aspects, the pitfalls and risks of Bayesian approaches are hardly introduced to proteomic investigators. Among them, one is sufficiently important to be brought to attention: namely, the possibility that priors introduced at an early stage of the computational investigations detrimentally influence the final statistical significance.