Authors:
Lorenzo Casini
;
Phyllis McKay Illari
;
Federica Russo
and
Jon Williamson
Affiliation:
University of Kent, United Kingdom
Keyword(s):
Bayesian network, Recursive Bayesian network, Prediction, Explanation, Control, Mechanism, Causation, Causality, Cancer, DNA damage response.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Computational Intelligence
;
Genomics and Proteomics
;
Soft Computing
;
Structural Bioinformatics
Abstract:
The Recursive Bayesian Net formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of physical mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations are vital for prediction, explanation and control respectively, a recursive Bayesian net can be applied to all these tasks.
We show how a Recursive Bayesian Net can be used to model mechanisms in cancer science. The highest level of the proposed model will contain variables at the clinical level, while a middle level will map the structure of the DNA damage response mechanism and the lowest level will contain information about gene expression.