NDLI: Optimal cancer prognosis under network uncertainty

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Optimal cancer prognosis under network uncertainty

Content Provider	ACM Digital Library
Author	Dalton, Lori A. Yousefi, Mohammadmahdi R.
Abstract	Typically, a vast amount of experience and data is needed to successfully determine cancer prognosis in the face of (1) the inherent stochasticity of cell dynamics, (2) incomplete knowledge of healthy cell regulation, and (3) the inherent uncertain and evolving nature of cancer progression. There is hope that models of cell regulation could be used to predict disease progression and successful treatment strategies, but there has been little work focusing on the third source of uncertainty above. In this work we investigate the impact of this kind of network uncertainty in predicting cancer prognosis. In particular, we focus on a scenario in which the precise aberrant regulatory relationships between genes in a patient are unknown, but the patient gene regulatory network is contained in an uncertainty class of possible mutations of some known healthy network. We optimistically assume that the probabilities of these abnormal networks are available, along with the best treatment for each network. Then, given a snapshot of the patient gene activity profile at a single moment in time, we study what can be said regarding a patient's treatability and prognosis. Our methodology is based on recent developments on optimal control strategies for probabilistic Boolean networks and optimal Bayesian classification. We show that in some circumstances, prognosis prediction may be highly unreliable, even in this optimistic setting with perfect knowledge of healthy biological processes and ideal treatment decisions.
Starting Page	682
Ending Page	683
Page Count	2
File Format	PDF
ISBN	9781450328944
DOI	10.1145/2649387.2660827
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Bayesian classification Cancer prognosis Network modeling
Content Type	Text
Resource Type	Article

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