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BCM: toolkit for Bayesian analysis of Computational Models using samplers

Overview of attention for article published in BMC Systems Biology, October 2016
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1 tweeter

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Title
BCM: toolkit for Bayesian analysis of Computational Models using samplers
Published in
BMC Systems Biology, October 2016
DOI 10.1186/s12918-016-0339-3
Pubmed ID
Authors

Bram Thijssen, Tjeerd M. H. Dijkstra, Tom Heskes, Lodewyk F. A. Wessels

Abstract

Computational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and disadvantages. It is typically unclear, before starting an analysis, which algorithm will perform well on a given computational model. We present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved. BCM represents an efficient one-stop-shop for computational modelers wishing to use sampler-based Bayesian statistics.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 2%
Unknown 42 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 28%
Student > Ph. D. Student 10 23%
Student > Postgraduate 4 9%
Student > Master 4 9%
Professor > Associate Professor 3 7%
Other 6 14%
Unknown 4 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 26%
Biochemistry, Genetics and Molecular Biology 9 21%
Engineering 6 14%
Computer Science 4 9%
Social Sciences 3 7%
Other 6 14%
Unknown 4 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 October 2016.
All research outputs
#7,400,562
of 8,566,293 outputs
Outputs from BMC Systems Biology
#768
of 868 outputs
Outputs of similar age
#203,769
of 249,436 outputs
Outputs of similar age from BMC Systems Biology
#17
of 17 outputs
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So far Altmetric has tracked 868 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.