Bayes Theorem
"Bayes Theorem" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
A theorem in probability theory named for Thomas Bayes (17021761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihoods of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.
Descriptor ID 
D001499

MeSH Number(s) 
E05.318.740.600.200 N05.715.360.750.625.150 N06.850.520.830.600.200

Concept/Terms 

Below are MeSH descriptors whose meaning is more general than "Bayes Theorem".
Below are MeSH descriptors whose meaning is more specific than "Bayes Theorem".
This graph shows the total number of publications written about "Bayes Theorem" by people in this website by year, and whether "Bayes Theorem" was a major or minor topic of these publications.
To see the data from this visualization as text, click here.
Year  Major Topic  Minor Topic  Total 

1985  0  1  1  1992  1  0  1  1996  0  2  2  1997  0  1  1  1998  0  2  2  1999  2  0  2  2000  1  0  1  2001  1  3  4  2002  0  1  1  2003  1  3  4  2004  1  7  8  2005  1  9  10  2006  1  10  11  2007  3  8  11  2008  2  12  14  2009  1  10  11  2010  2  11  13  2011  1  13  14  2012  0  17  17  2013  1  16  17  2014  1  14  15  2015  0  11  11  2016  3  11  14  2017  3  8  11  2018  3  10  13  2019  0  3  3 
To return to the timeline, click here.
Below are the most recent publications written about "Bayes Theorem" by people in Profiles.

Turchin MC, Stephens M. Bayesian multivariate reanalysis of large genetic studies identifies many new associations. PLoS Genet. 2019 10; 15(10):e1008431.

Wang Q, Chen R, Cheng F, Wei Q, Ji Y, Yang H, Zhong X, Tao R, Wen Z, Sutcliffe JS, Liu C, Cook EH, Cox NJ, Li B. A Bayesian framework that integrates multiomics data and gene networks predicts risk genes from schizophrenia GWAS data. Nat Neurosci. 2019 05; 22(5):691699.

Nakagome S, Hudson RR, Di Rienzo A. Inferring the model and onset of natural selection under varying population size from the site frequency spectrum and haplotype structure. Proc Biol Sci. 2019 02 13; 286(1896):20182541.

Jumper JM, Faruk NF, Freed KF, Sosnick TR. Trajectorybased training enables protein simulations with accurate folding and Boltzmann ensembles in cpuhours. PLoS Comput Biol. 2018 12; 14(12):e1006578.

Didelot X, Croucher NJ, Bentley SD, Harris SR, Wilson DJ. Bayesian inference of ancestral dates on bacterial phylogenetic trees. Nucleic Acids Res. 2018 12 14; 46(22):e134.

Dey KK, Xie D, Stephens M. A new sequence logo plot to highlight enrichment and depletion. BMC Bioinformatics. 2018 Dec 10; 19(1):473.

Zhu X, Stephens M. Largescale genomewide enrichment analyses identify new traitassociated genes and pathways across 31 human phenotypes. Nat Commun. 2018 10 19; 9(1):4361.

Blake LE, Thomas SM, Blischak JD, Hsiao CJ, Chavarria C, Myrthil M, Gilad Y, Pavlovic BJ. A comparative study of endoderm differentiation in humans and chimpanzees. Genome Biol. 2018 10 15; 19(1):162.

Hutchison AL, Allada R, Dinner AR. Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data. J Biol Rhythms. 2018 08; 33(4):339349.

Gray CL, Lobdell DT, Rappazzo KM, Jian Y, Jagai JS, Messer LC, Patel AP, DeFlorioBarker SA, Lyttle C, Solway J, Rzhetsky A. Associations between environmental quality and adult asthma prevalence in medical claims data. Environ Res. 2018 10; 166:529536.

People People who have written about this concept. _
Similar Concepts
People who have written about this concept.
_
Top Journals
Top journals in which articles about this concept have been published.
