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OBC specifications


Unique identifier OMICS_28346
Name OBC
Alternative name Optimal Bayesian Classification
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Julia
License MIT License
Computer skills Advanced
Stability Stable
Maintained Yes



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  • person_outline Jason Knight <>

Publication for Optimal Bayesian Classification

OBC in publications

PMCID: 5751802
PMID: 29297278
DOI: 10.1186/s12859-017-1893-4

[…] 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 […]

PMCID: 5751547
PMID: 29297277
DOI: 10.1186/s12859-017-1887-2

[…] vitamin e analogs, tocoflexols with enhanced bioavailability using in silico approaches., third place: , name: shahin boluki., university: texas a&m university., title: prior construction for optimal bayesian classification using unlabeled data., poster session award: , the poster session was held at the end of the first day of the meeting. student and post-doctoral presenters presented […]

PMCID: 4844301
PMID: 27127404
DOI: 10.4137/CIN.S30801

[…] outcomes, and partitioning networks in the uncertainty class into prognosis classes based on these scores. for a new patient, the probability distribution of the prognosis class was evaluated using optimal bayesian classification, given patient data. it was assumed that (1) disease is the result of several mutations of a known healthy network and that these mutations and their probability […]

PMCID: 4720709
PMID: 26834782
DOI: 10.1186/s13637-016-0036-y

[…] are collected over time processes. given certain gaussian assumptions, classification in the sde setting takes the same form as ordinary classification in the gaussian model and we can apply the optimal bayesian classification theory once we have a prior distribution constructed in accordance with known stochastic equations. in this paper, we provide the mathematical framework to synthesize […]

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OBC institution(s)
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA; Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA; Department of Biology, Texas A&M University, College Station, TX, USA; Departments of Nutrition & Food Science, and Biochemistry and Biophysics, Texas A&M University, College Station, TX, USA
OBC funding source(s)
Supported by the National Institute of Health grant U01CA162077, the NIEHS Center for Translational Environmental Health Research (CTEHR) P30ES023512, and the NIH Research Supplement to Promote Diversity in Health-Related Research (CA-129444).

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