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Bicluster Sampled Coherence Metric (BSCM) provides an accurate environmental context for phenotype predictions

Overview of attention for article published in BMC Systems Biology, April 2015
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Title
Bicluster Sampled Coherence Metric (BSCM) provides an accurate environmental context for phenotype predictions
Published in
BMC Systems Biology, April 2015
DOI 10.1186/1752-0509-9-s2-s1
Pubmed ID
Authors

Samuel A Danziger, David J Reiss, Alexander V Ratushny, Jennifer J Smith, Christopher L Plaisier, John D Aitchison, Nitin S Baliga

Abstract

Biclustering is a popular method for identifying under which experimental conditions biological signatures are co-expressed. However, the general biclustering problem is NP-hard, offering room to focus algorithms on specific biological tasks. We hypothesize that conditional co-regulation of genes is a key factor in determining cell phenotype and that accurately segregating conditions in biclusters will improve such predictions. Thus, we developed a bicluster sampled coherence metric (BSCM) for determining which conditions and signals should be included in a bicluster. Our BSCM calculates condition and cluster size specific p-values, and we incorporated these into the popular integrated biclustering algorithm cMonkey. We demonstrate that incorporation of our new algorithm significantly improves bicluster co-regulation scores (p-value = 0.009) and GO annotation scores (p-value = 0.004). Additionally, we used a bicluster based signal to predict whether a given experimental condition will result in yeast peroxisome induction. Using the new algorithm, the classifier accuracy improves from 41.9% to 76.1% correct. We demonstrate that the proposed BSCM helps determine which signals ought to be co-clustered, resulting in more accurately assigned bicluster membership. Furthermore, we show that BSCM can be extended to more accurately detect under which experimental conditions the genes are co-clustered. Features derived from this more accurate analysis of conditional regulation results in a dramatic improvement in the ability to predict a cellular phenotype in yeast. The latest cMonkey is available for download at https://github.com/baliga-lab/cmonkey2. The experimental data and source code featured in this paper is available http://AitchisonLab.com/BSCM. BSCM has been incorporated in the official cMonkey release.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Singapore 1 4%
Unknown 22 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 39%
Student > Master 4 17%
Student > Ph. D. Student 3 13%
Other 2 9%
Professor 1 4%
Other 1 4%
Unknown 3 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 30%
Agricultural and Biological Sciences 6 26%
Computer Science 3 13%
Environmental Science 2 9%
Social Sciences 1 4%
Other 3 13%
Unknown 1 4%
Attention Score in Context

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 17 April 2015.
All research outputs
#19,017,658
of 23,577,761 outputs
Outputs from BMC Systems Biology
#834
of 1,143 outputs
Outputs of similar age
#194,665
of 265,488 outputs
Outputs of similar age from BMC Systems Biology
#11
of 13 outputs
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So far Altmetric has tracked 1,143 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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