RECURSIVE BAYESIAN NETS FOR PREDICTION, EXPLANATION AND CONTROL IN CANCER SCIENCE - A Position Paper

Lorenzo Casini, Phyllis McKay Illari, Federica Russo, Jon Williamson

2010

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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Paper Citation


in Harvard Style

Casini L., McKay Illari P., Russo F. and Williamson J. (2010). RECURSIVE BAYESIAN NETS FOR PREDICTION, EXPLANATION AND CONTROL IN CANCER SCIENCE - A Position Paper . In Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010) ISBN 978-989-674-019-1, pages 233-238. DOI: 10.5220/0002744902330238


in Bibtex Style

@conference{bioinformatics10,
author={Lorenzo Casini and Phyllis McKay Illari and Federica Russo and Jon Williamson},
title={RECURSIVE BAYESIAN NETS FOR PREDICTION, EXPLANATION AND CONTROL IN CANCER SCIENCE - A Position Paper},
booktitle={Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010)},
year={2010},
pages={233-238},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002744902330238},
isbn={978-989-674-019-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010)
TI - RECURSIVE BAYESIAN NETS FOR PREDICTION, EXPLANATION AND CONTROL IN CANCER SCIENCE - A Position Paper
SN - 978-989-674-019-1
AU - Casini L.
AU - McKay Illari P.
AU - Russo F.
AU - Williamson J.
PY - 2010
SP - 233
EP - 238
DO - 10.5220/0002744902330238