KeySDL: Sparse Dictionary Learning for Keystone Microbe Identification.

ABSTRACT: Identification of microbes with large impacts on their microbial communities, known as keystone microbes, is a topic of long-standing interest in microbiome research. However, many approaches to identify keystone microbes are limited by the inherent nonlinearity and state-dependence of microbial dynamics. Machine learning approaches have been applied to address these shortcomings but often require more data than is available for a given microbial system. We propose a keystone identification approach called KeySDL which reduces the amount of data required by incorporating assumptions about the type of microbial dynamics present in the experimental system. The data are modeled as originating from a Generalized Lotka-Volterra (GLV) model, an architecture commonly used to simulate microbial sy

SUBMITTER: Gordon M 

PROVIDER: S-EPMC12363843 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature