netReg: network-regularized linear models for biological association studies

Dirmeier S, Fuchs C, Mueller NS, Theis FJ (2017)
Bioinformatics 34(5): 896-898.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor/in
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Abstract / Bemerkung
Modelling biological associations or dependencies using linear regression is often complicated when the analyzed data-sets are high-dimensional and less observations than variables are available (n ≪ p). For genomic data-sets penalized regression methods have been applied settling this issue. Recently proposed regression models utilize prior knowledge on dependencies, e.g. in the form of graphs, arguing that this information will lead to more reliable estimates for regression coefficients. However, none of the proposed models for multivariate genomic response variables have been implemented as a computationally efficient, freely available library. In this paper we propose netReg, a package for graph-penalized regression models that use large networks and thousands of variables. netReg incorporates a priori generated biological graph information into linear models yielding sparse or smooth solutions for regression coefficients.
Erscheinungsjahr
2017
Zeitschriftentitel
Bioinformatics
Band
34
Ausgabe
5
Seite(n)
896-898
ISSN
1367-4803
eISSN
1460-2059
Page URI
https://pub.uni-bielefeld.de/record/2933892

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Dirmeier S, Fuchs C, Mueller NS, Theis FJ. netReg: network-regularized linear models for biological association studies. Bioinformatics. 2017;34(5):896-898.
Dirmeier, S., Fuchs, C., Mueller, N. S., & Theis, F. J. (2017). netReg: network-regularized linear models for biological association studies. Bioinformatics, 34(5), 896-898. doi:10.1093/bioinformatics/btx677
Dirmeier, S., Fuchs, C., Mueller, N. S., and Theis, F. J. (2017). netReg: network-regularized linear models for biological association studies. Bioinformatics 34, 896-898.
Dirmeier, S., et al., 2017. netReg: network-regularized linear models for biological association studies. Bioinformatics, 34(5), p 896-898.
S. Dirmeier, et al., “netReg: network-regularized linear models for biological association studies”, Bioinformatics, vol. 34, 2017, pp. 896-898.
Dirmeier, S., Fuchs, C., Mueller, N.S., Theis, F.J.: netReg: network-regularized linear models for biological association studies. Bioinformatics. 34, 896-898 (2017).
Dirmeier, Simon, Fuchs, Christiane, Mueller, Nikola S, and Theis, Fabian J. “netReg: network-regularized linear models for biological association studies”. Bioinformatics 34.5 (2017): 896-898.

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