Understanding virtual patients efficiently and rigorously by combining machine learning with dynamical modelling.

ABSTRACT: Individual biological organisms are characterized by daunting heterogeneity, which precludes describing or understanding populations of 'patients' with a single mathematical model. Recently, the field of quantitative systems pharmacology (QSP) has adopted the notion of virtual patients (VPs) to cope with this challenge. A typical population of VPs represents the behavior of a heterogeneous patient population with a distribution of parameter values over a mathematical model of fixed structure. Though this notion of VPs is a powerful tool to describe patients' heterogeneity, the analysis and understanding of these VPs present new challenges to systems pharmacologists. Here, using a model of the hypothalamic-pituitary-adrenal axis, we show that an integrated pipeline that combines machine lea

SUBMITTER: Zhang T 

PROVIDER: S-EPMC8837571 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature