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Study of Meta-analysis strategies for network inference using information-theoretic approaches

Overview of attention for article published in BioData Mining, May 2017
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Title
Study of Meta-analysis strategies for network inference using information-theoretic approaches
Published in
BioData Mining, May 2017
DOI 10.1186/s13040-017-0136-6
Pubmed ID
Authors

Ngoc C. Pham, Benjamin Haibe-Kains, Pau Bellot, Gianluca Bontempi, Patrick E. Meyer

Abstract

Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches, which suffer from experimental biases and the low number of samples by analysing individual datasets. To date, there are mainly two strategies for the problem of interest: the first one ("data merging") merges all datasets together and then infers a GRN whereas the other ("networks ensemble") infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. In this work, we are going to present another meta-analysis approach for inferring GRNs from multiple studies. Our proposed meta-analysis approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix. Afterwards, we evaluate the performance of the two commonly used approaches mentioned above and our presented approach with a systematic set of experiments based on in silico benchmarks. We proposed a first systematic evaluation of different strategies for reverse engineering GRNs from multiple datasets. Experiment results strongly suggest that assembling matrices of pairwise dependencies is a better strategy for network inference than the two commonly used ones.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 53%
Professor > Associate Professor 3 20%
Professor 1 7%
Student > Ph. D. Student 1 7%
Student > Bachelor 1 7%
Other 0 0%
Unknown 1 7%
Readers by discipline Count As %
Computer Science 4 27%
Biochemistry, Genetics and Molecular Biology 3 20%
Agricultural and Biological Sciences 2 13%
Medicine and Dentistry 2 13%
Physics and Astronomy 1 7%
Other 2 13%
Unknown 1 7%
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 09 July 2017.
All research outputs
#17,890,958
of 22,968,808 outputs
Outputs from BioData Mining
#249
of 308 outputs
Outputs of similar age
#221,708
of 310,492 outputs
Outputs of similar age from BioData Mining
#6
of 8 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 308 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.