Interpretable modeling of genotype-phenotype landscapes with state-of-the-art predictive power.

ABSTRACT: Large-scale measurements linking genetic background to biological function have driven a need for models that can incorporate these data for reliable predictions and insight into the underlying biophysical system. Recent modeling efforts, however, prioritize predictive accuracy at the expense of model interpretability. Here, we present LANTERN (landscape interpretable nonparametric model, https://github.com/usnistgov/lantern), a hierarchical Bayesian model that distills genotype-phenotype landscape (GPL) measurements into a low-dimensional feature space that represents the fundamental biological mechanisms of the system while also enabling straightforward, explainable predictions. Across a benchmark of large-scale datasets, LANTERN equals or outperforms all alternative approaches, includin

SUBMITTER: Tonner PD 

PROVIDER: S-EPMC9245639 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature