Machine learning approaches are now an important component of the life scientist’s toolkit. From just a cursory review of the evidence, it’s clear that ML tools have enabled us to solve once intractable problems like genetic variant effect prediction1, protein folding2, and unknown perturbation inference3. As this new class of models enters more and more branches of life science, a natural tension has arisen between the empirical mode of inquiry enabled by ML and the traditional, analytical and heuristic approach of molecular biology. This tension is visible in the back-and-forth discourse over the role of ML in biology, with ML practitioners sometimes overstating the capabilities that models provide, and experimental biologists emphasizing the failure modes of ML models while often overlooking their strengths.
— Read on jck.bio/learning-representations-of-life/