Machine learning in biological physics: From biomolecular prediction to design.

ABSTRACT: Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in

SUBMITTER: Martin J 

PROVIDER: S-EPMC11228481 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature