Unsupervised logic-based mechanism inference for network-driven biological processes.

ABSTRACT: Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity

SUBMITTER: Prugger M 

PROVIDER: S-EPMC8202945 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature