Can I read Innovations in Integrating Machine Learning and Agent-Based Modeling of Biomedical Systems on EtoBox?

Innovations in Integrating Machine Learning and Agent-Based Modeling of Biomedical Systems by Sivakumar, Nikita; Mura, Cameron; Peirce, Shayn M. is a scholarly article available to read on EtoBox.

What is Innovations in Integrating Machine Learning and Agent-Based Modeling of Biomedical Systems about?

Agent-based modeling (ABM) is a well-established paradigm for simulating complex systems via interactions between constituent entities. Machine learning (ML) refers to approaches whereby statistical algorithms 'learn' from data on their own, without imposing a priori theories of system behavior. Biological systems -- from molecules, to cells, to entire organisms -- consist of vast numbers of entities, governed by complex webs of interactions that span many spatiotemporal scales and exhibit nonlinearity, stochasticity and intricate coupling between entities. The macroscopic properties and collective dynamics of such systems are difficult to capture via continuum modelling and mean-field formalisms. ABM takes a 'bottom-up' approach that obviates these difficulties by enabling one to easily propose and test a set of well-defined 'rules' to be applied to the individual entities (agents) in a system. Evaluating a system and propagating its state over discrete time-steps effectively simulates the system, allowing observables to be computed and system properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, there is

Author

Sivakumar, Nikita; Mura, Cameron; Peirce, Shayn M.

Published

2022

Language

EN