netbenchmark.geek-lab.org

netbenchmark

How to use it. Is an open-source R/Bioconductor package that implements a broad benchmarking of unsupervised. Network inference methods from data. It is focused on, but not limited to,. The package was written by Pau Bellot. And Patrick E. Meyer. In the BioSys Group of the Université de Liège. In collaboration with the GPI group of Universitat Politècnica de Catalunya. Is maintained by Pau Bellot. And licensed under a. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.

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How to use it. Is an open-source R/Bioconductor package that implements a broad benchmarking of unsupervised. Network inference methods from data. It is focused on, but not limited to,. The package was written by Pau Bellot. And Patrick E. Meyer. In the BioSys Group of the Université de Liège. In collaboration with the GPI group of Universitat Politècnica de Catalunya. Is maintained by Pau Bellot. And licensed under a. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.

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netbenchmark | netbenchmark.geek-lab.org Reviews

https://netbenchmark.geek-lab.org

How to use it. Is an open-source R/Bioconductor package that implements a broad benchmarking of unsupervised. Network inference methods from data. It is focused on, but not limited to,. The package was written by Pau Bellot. And Patrick E. Meyer. In the BioSys Group of the Université de Liège. In collaboration with the GPI group of Universitat Politècnica de Catalunya. Is maintained by Pau Bellot. And licensed under a. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.

INTERNAL PAGES

netbenchmark.geek-lab.org

1

Extending the Package - netbenchmark

http://netbenchmark.geek-lab.org/extending-the-package

How to use it. Adding an inference method. The aim of the package is that users can compare new methods with the state-of-the-art methods, allowing new approaches to be evaluated. Currently, the network inference methods that can be evaluated should work with steady state data. And should be able to infer networks with several thousands of variables. And small number of samples. If you have a new method that. Fulfills these requirements, and you want it to be added in the package and in the results page.

2

Citation - netbenchmark

http://netbenchmark.geek-lab.org/citation

How to use it. Citation (from within R, enter citation("netbenchmark") :. NetBenchmark: A Bioconductor Package for Reproducible Benchmarks of Gene Regulatory Network Inference. Pau Bellot, Catharina Olsen, Philippe Salembier, Albert Oliveras-Verges and. Patrick E. Meyer.

3

Results - netbenchmark

http://netbenchmark.geek-lab.org/results

How to use it. Here, we present the main results of the study. In order to obtain them we have used the following R code:. Top20aupr - netbenchmark( methods="all", local.noise=20,. Global.noise=10, noiseType=c("normal","lognormal"),. Performances of the various GRN inference methods on the datasources.

4

References - netbenchmark

http://netbenchmark.geek-lab.org/results/references

How to use it. Margolin, Adam A., et al. "ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context." BMC bioinformatics7.Suppl 1 (2006): S7. Altay, Gökmen, and Frank Emmert-Streib. "Inferring the conservative causal core of gene regulatory networks." BMC Systems Biology 4.1 (2010): 132. Irrthum, Alexandre, Louis Wehenkel, and Pierre Geurts. "Inferring regulatory networks from expression data using tree-based methods." PloS one 5.9 (2010): e12776. Reverter, A...

5

How to use it - netbenchmark

http://netbenchmark.geek-lab.org/how-to-use-it

How to use it. How to use it. In the last decade, several methods have tackled the challenge of reconstructing gene regulatory networks from gene expression data. Several papers have compared and evaluated the different network inference methods relying on simulated data. This is a new comparison that assesses different methods in a high-heterogeneity data scenario which could reveal the specialization of methods for the different network types and data. Toy example for main benchmark:. Pearsoncor - func...

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Software - Patrick E. Meyer

http://homepage.meyerp.com/software

Patrick E. Meyer. MINET: An Open Source Bioconductor Package for Network Inference using Mutual Information. INFOTHEO: An Open Source R Package of Information-Theoretic Measures. Flynet: Network inference from modEncode data. C Feature Selection Package: MASSIVE.

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netbenchmark.geek-lab.org

netbenchmark

How to use it. Is an open-source R/Bioconductor package that implements a broad benchmarking of unsupervised. Network inference methods from data. It is focused on, but not limited to,. The package was written by Pau Bellot. And Patrick E. Meyer. In the BioSys Group of the Université de Liège. In collaboration with the GPI group of Universitat Politècnica de Catalunya. Is maintained by Pau Bellot. And licensed under a. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.

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