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Network-based biomarkers enhance classical approaches to prognostic gene expression signatures

Overview of attention for article published in BMC Systems Biology, December 2014
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1 tweeter

Citations

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Title
Network-based biomarkers enhance classical approaches to prognostic gene expression signatures
Published in
BMC Systems Biology, December 2014
DOI 10.1186/1752-0509-8-s4-s5
Pubmed ID
Authors

Rebecca L Barter, Sarah-Jane Schramm, Graham J Mann, Yee Hwa Yang

Abstract

Classical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An open problem is whether such approaches add value to the more traditional methods of signature modelling. We explored this question via comparison of the most widely employed single-gene, gene-set, and network-based methods, using gene expression microarray data from two different cancers: melanoma and ovarian. We considered two kinds of network approaches. The first of these identifies informative genes using gene expression and network connectivity information combined, the latter drawn from prior knowledge of protein-protein interactions. The second approach focuses on identification of informative sub-networks (small networks of interacting proteins, again from prior knowledge networks). For all methods we performed 100 rounds of 5-fold cross-validation under 3 different classifiers. For network-based approaches, we considered two different protein-protein interaction networks. We quantified resulting patterns of misclassification and discussed the relative value of each relative to ongoing development of prognostic biomarkers.

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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 2%
Germany 1 2%
Unknown 39 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 34%
Researcher 13 32%
Student > Master 4 10%
Student > Postgraduate 2 5%
Professor 1 2%
Other 4 10%
Unknown 3 7%
Readers by discipline Count As %
Computer Science 10 24%
Agricultural and Biological Sciences 8 20%
Biochemistry, Genetics and Molecular Biology 6 15%
Mathematics 3 7%
Medicine and Dentistry 3 7%
Other 6 15%
Unknown 5 12%

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 19 December 2014.
All research outputs
#3,799,732
of 4,691,823 outputs
Outputs from BMC Systems Biology
#612
of 701 outputs
Outputs of similar age
#121,215
of 154,696 outputs
Outputs of similar age from BMC Systems Biology
#31
of 42 outputs
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