Incorporating biological prior knowledge for Bayesian learning via maximal knowledge-driven information priors

BMC Bioinformatics
Shahin BolukiEdward R Dougherty

Abstract

Phenotypic classification is problematic because small samples are ubiquitous; and, for these, use of prior knowledge is critical. If knowledge concerning the feature-label distribution - for instance, genetic pathways - is available, then it can be used in learning. Optimal Bayesian classification provides optimal classification under model uncertainty. It differs from classical Bayesian methods in which a classification model is assumed and prior distributions are placed on model parameters. With optimal Bayesian classification, uncertainty is treated directly on the feature-label distribution, which assures full utilization of prior knowledge and is guaranteed to outperform classical methods. The salient problem confronting optimal Bayesian classification is prior construction. In this paper, we propose a new prior construction methodology based on a general framework of constraints in the form of conditional probability statements. We call this prior the maximal knowledge-driven information prior (MKDIP). The new constraint framework is more flexible than our previous methods as it naturally handles the potential inconsistency in archived regulatory relationships and conditioning can be augmented by other knowledge, such as...Continue Reading

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Citations

Apr 25, 2018·Bioinformatics·Siamak Zamani DadanehXiaoning Qian
Jun 21, 2019·BMC Bioinformatics·Shahin BolukiEdward R Dougherty
Sep 10, 2020·BMC Genomics·Siamak Zamani DadanehXiaoning Qian
Dec 24, 2018·BMC Systems Biology·Roozbeh DehghannasiriEdward R Dougherty
Mar 5, 2019·Metabolomics : Official Journal of the Metabolomic Society·Azam YazdaniAhmad Samiei
Nov 3, 2020·IScience·Changyu DengWei Lu
Apr 7, 2021·Bioinformatics·Shahin BolukiEdward R Dougherty
Aug 24, 2018··Xiaoning QianShahin Boluki

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Methods Mentioned

BETA
RNA-Seq

Software Mentioned

PDCOTP
cBioPortal
R
mathcal
MATLAB
RMDIP
MKDIP
MutSig
RMEP
CycE

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