Recursive Bayesian Nets for Prediction, Explanation and Control in Cancer Science - A Position Paper

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.

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Showing 1-10 of 15 references

Bayesian nets and causality: philosophical and computational foundations

  • J Williamson
  • 2005
Highly Influential
5 Excerpts

Causality and causal modelling in the social sciences. Measuring variations. Methodos Series

  • F Russo
  • 2008
2 Excerpts

Explaining the brain

  • C F Craver
  • 2007
1 Excerpt

Strategies for discovering mechanisms: Schema instantiation, modular subassembly, forward/backward chaining

  • L Darden
  • 2002
1 Excerpt