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Survival models with preclustered gene groups as covariates

Overview of attention for article published in BMC Bioinformatics, December 2011
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1 tweeter

Citations

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40 Mendeley
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Title
Survival models with preclustered gene groups as covariates
Published in
BMC Bioinformatics, December 2011
DOI 10.1186/1471-2105-12-478
Pubmed ID
Authors

Kai Kammers, Michel Lang, Jan G Hengstler, Marcus Schmidt, Jörg Rahnenführer

Abstract

An important application of high dimensional gene expression measurements is the risk prediction and the interpretation of the variables in the resulting survival models. A major problem in this context is the typically large number of genes compared to the number of observations (individuals). Feature selection procedures can generate predictive models with high prediction accuracy and at the same time low model complexity. However, interpretability of the resulting models is still limited due to little knowledge on many of the remaining selected genes. Thus, we summarize genes as gene groups defined by the hierarchically structured Gene Ontology (GO) and include these gene groups as covariates in the hazard regression models. Since expression profiles within GO groups are often heterogeneous, we present a new method to obtain subgroups with coherent patterns. We apply preclustering to genes within GO groups according to the correlation of their gene expression measurements.

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

Geographical breakdown

Country Count As %
Mexico 1 3%
United States 1 3%
Germany 1 3%
Unknown 37 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 30%
Researcher 9 23%
Student > Master 4 10%
Professor 3 8%
Student > Doctoral Student 2 5%
Other 5 13%
Unknown 5 13%
Readers by discipline Count As %
Computer Science 9 23%
Medicine and Dentistry 6 15%
Mathematics 6 15%
Agricultural and Biological Sciences 5 13%
Physics and Astronomy 2 5%
Other 6 15%
Unknown 6 15%

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 20 December 2011.
All research outputs
#9,906,144
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,816
of 4,576 outputs
Outputs of similar age
#157,054
of 221,652 outputs
Outputs of similar age from BMC Bioinformatics
#148
of 178 outputs
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