Proceedings Article10.1109/ACSSC.2013.6810526
Sample-based prior probability construction using biological pathway knowledge
Mohammad Shahrokh Esfahani,Edward R. Dougherty +1 more
- 01 Nov 2013
- pp 1405-1409
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TL;DR: This paper addresses the problem of prior probability construction by proposing a series of optimization paradigms that utilize the incomplete prior information contained in pathways in the special case of a Normal-Wishart prior distribution on the mean and inverse covariance matrix of a Gaussian distribution.
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Abstract: Small samples are commonplace in genomic/proteomic classification, the result being inadequate classifier design and poor error estimation. The problem has recently been addressed by utilizing prior knowledge in the form of a prior distribution on an uncertainty class of feature-label distributions. A critical issue remains: how to incorporate biological knowledge into the prior distribution. For genomics/proteomics, the most common kind of knowledge is in the form of signaling pathways. In this paper, we address the problem of prior probability construction by proposing a series of optimization paradigms that utilize the incomplete prior information contained in pathways. In the special case of a Normal-Wishart prior distribution on the mean and inverse covariance matrix (precision matrix) of a Gaussian distribution, these optimization problems become convex.
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Citations
An Optimization-Based Framework for the Transformation of Incomplete Biological Knowledge into a Probabilistic Structure and Its Application to the Utilization of Gene/Protein Signaling Pathways in Discrete Phenotype Classification
TL;DR: This paper provides a framework and the mathematical tools to transform signaling pathways to prior probabilities governing uncertainty classes of feature-label distributions used in classifier design and proposes optimization paradigms to estimate the parameters of a Dirichlet distribution in the Bayesian setting.
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