Nagl, Sylvia and Williams, Matthew and El-Mehidi, Nadjet and Patkar, Vivek and Williamson, J. (2006) Objective Bayesian nets for integrating cancer knowledge: a systems biology approach. In: Proceedings of the Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology. Helsinki University Printing House pp. 44-49.
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According to objective Bayesianism, an agent’s degrees of belief should be determined by a probability function, out of all those that satisfy constraints imposed by background knowledge, that maximises entropy. A Bayesian net offers a way of efficiently representing a probability function and efficiently drawing inferences from that function. An objective Bayesian net is a Bayesian net representation of the maximum entropy probability function. In this paper we apply the machinery of objective Bayesian nets to breast cancer prognosis. Background knowledge is diverse and comes from several different sources: a database of clinical data, a database of molecular data, and quantitative data from the literature. We show how an objective Bayesian net can be constructed from this background knowledge and how it can be applied to yield prognoses and aid translation of clinical knowledge to genomics research.
|Item Type:||Conference or workshop item (Paper)|
|Subjects:||R Medicine > R Medicine (General)
B Philosophy. Psychology. Religion > B Philosophy (General)
Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QH Natural history > QH301 Biology
|Divisions:||Faculties > Humanities > School of European Culture and Languages|
|Depositing User:||Jon Williamson|
|Date Deposited:||26 Nov 2008 23:11|
|Last Modified:||20 Jan 2012 15:00|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/7448 (The current URI for this page, for reference purposes)|
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