MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets.

ABSTRACT: Computational models that can explain and predict complex sub-cellular, cellular, and tissue-level drug response mechanisms could speed drug discovery and prioritize patient-specific treatments (i.e., precision medicine). Some models are mechanistic with detailed equations describing known (or supposed) physicochemical processes, while some are statistical or machine learning-based approaches, that explain datasets but have no mechanistic or causal guarantees. These two types of modeling are rarely combined, missing the opportunity to explore possibly causal but data-driven new knowledge while explaining what is already known. Here, we explore combining machine learned associations with mechanistic models to develop computational models that could more fully represent cellular behavior. In

SUBMITTER: Erdem C 

PROVIDER: S-EPMC10807051 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature