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2017 ; 18
(1
): 199
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Significance evaluation in factor graphs
#MMPMID28359297
Madsen T
; Hobolth A
; Jensen JL
; Pedersen JS
BMC Bioinformatics
2017[Mar]; 18
(1
): 199
PMID28359297
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BACKGROUND: Factor graphs provide a flexible and general framework for specifying
probability distributions. They can capture a range of popular and recent models
for analysis of both genomics data as well as data from other scientific fields.
Owing to the ever larger data sets encountered in genomics and the
multiple-testing issues accompanying them, accurate significance evaluation is of
great importance. We here address the problem of evaluating statistical
significance of observations from factor graph models. RESULTS: Two novel
numerical approximations for evaluation of statistical significance are
presented. First a method using importance sampling. Second a saddlepoint
approximation based method. We develop algorithms to efficiently compute the
approximations and compare them to naive sampling and the normal approximation.
The individual merits of the methods are analysed both from a theoretical
viewpoint and with simulations. A guideline for choosing between the normal
approximation, saddle-point approximation and importance sampling is also
provided. Finally, the applicability of the methods is demonstrated with examples
from cancer genomics, motif-analysis and phylogenetics. CONCLUSIONS: The
applicability of saddlepoint approximation and importance sampling is
demonstrated on known models in the factor graph framework. Using the two methods
we can substantially improve computational cost without compromising accuracy.
This contribution allows analyses of large datasets in the general factor graph
framework.
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