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A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems

Overview of attention for article published in BMC Systems Biology, August 2015
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  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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3 tweeters

Citations

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5 Dimensions

Readers on

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36 Mendeley
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Title
A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems
Published in
BMC Systems Biology, August 2015
DOI 10.1186/s12918-015-0194-7
Pubmed ID
Authors

Weiwei Yin, Swetha Garimalla, Alberto Moreno, Mary R. Galinski, Mark P. Styczynski

Abstract

There are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). However, complex animal model systems typically have significant limitations on cohort sizes, number of samples, and the ability to perform follow-up and validation experiments. These constraints are particularly problematic for many current network learning approaches, which require large numbers of samples and may predict many more regulatory relationships than actually exist. Here, we test the idea that by leveraging the accuracy and efficiency of classifiers, we can construct high-quality networks that capture important interactions between variables in datasets with few samples. We start from a previously-developed tree-like Bayesian classifier and generalize its network learning approach to allow for arbitrary depth and complexity of tree-like networks. Using four diverse sample networks, we demonstrate that this approach performs consistently better at low sample sizes than the Sparse Candidate Algorithm, a representative approach for comparison because it is known to generate Bayesian networks with high positive predictive value. We develop and demonstrate a resampling-based approach to enable the identification of a viable root for the learned tree-like network, important for cases where the root of a network is not known a priori. We also develop and demonstrate an integrated resampling-based approach to the reduction of variable space for the learning of the network. Finally, we demonstrate the utility of this approach via the analysis of a transcriptional dataset of a malaria challenge in a non-human primate model system, Macaca mulatta, suggesting the potential to capture indicators of the earliest stages of cellular differentiation during leukopoiesis. We demonstrate that by starting from effective and efficient approaches for creating classifiers, we can identify interesting tree-like network structures with significant ability to capture the relationships in the training data. This approach represents a promising strategy for inferring networks with high positive predictive value under the constraint of small numbers of samples, meeting a need that will only continue to grow as more high-throughput studies are applied to complex model systems.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Brazil 1 3%
Unknown 34 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 19%
Student > Master 7 19%
Researcher 4 11%
Student > Bachelor 4 11%
Student > Postgraduate 3 8%
Other 6 17%
Unknown 5 14%
Readers by discipline Count As %
Computer Science 6 17%
Medicine and Dentistry 5 14%
Biochemistry, Genetics and Molecular Biology 5 14%
Agricultural and Biological Sciences 4 11%
Social Sciences 2 6%
Other 8 22%
Unknown 6 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 28 August 2015.
All research outputs
#2,468,107
of 5,560,729 outputs
Outputs from BMC Systems Biology
#300
of 758 outputs
Outputs of similar age
#82,361
of 194,919 outputs
Outputs of similar age from BMC Systems Biology
#17
of 33 outputs
Altmetric has tracked 5,560,729 research outputs across all sources so far. This one has received more attention than most of these and is in the 54th percentile.
So far Altmetric has tracked 758 research outputs from this source. They receive a mean Attention Score of 3.1. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 194,919 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.