Sample-Based Prior Probability Construction Using Biological Pathway Knowledge Conference Paper

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. 2013 IEEE.

name of conference

• 2013 Asilomar Conference on Signals, Systems and Computers

authors

• Dougherty, Edward

published proceedings

• 2013 ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS

author list (cited authors)

• Esfahani, M. S., & Dougherty, E. R.

citation count

• 1

complete list of authors

• Esfahani, Mohammad Shahrokh||Dougherty, Edward R

publication date

• November 2013

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Conference record / Asilomar Conference on Signals, Systems & Computers. Asilomar Conference on Signals, Systems & Computers  Journal

keywords

• Biological Pathway Knowledge
• Phenotype Classification
• Prior Probability Construction

Digital Object Identifier (DOI)

• 10.1109/acssc.2013.6810526

International Standard Book Number (ISBN) 13

• 9781479923908

start page

• 1405

end page

• 1409

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Facssc.2013.6810526