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Authors: Lorenzo Casini ; Phyllis McKay Illari ; Federica Russo and Jon Williamson

Affiliation: University of Kent, United Kingdom

ISBN: 978-989-674-019-1

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.

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Paper citation in several formats:
Casini L.; McKay Illari P.; Russo F.; Williamson J. and (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

@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},
}

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

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