000166759 001__ 166759
000166759 005__ 20190316235141.0
000166759 0247_ $$2doi$$a10.1093/bioinformatics/btr373
000166759 02470 $$2ISI$$a000293620800013
000166759 037__ $$aARTICLE
000166759 245__ $$aGeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods
000166759 269__ $$a2011
000166759 260__ $$bOxford University Press$$c2011
000166759 336__ $$aJournal Articles
000166759 500__ $$awingx
000166759 520__ $$aMotivation: Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks.
000166759 6531_ $$aReverse Engineering
000166759 6531_ $$aGene Regulatory Networks
000166759 6531_ $$aPerformance Assessment
000166759 6531_ $$aNetwork Motifs
000166759 6531_ $$aDREAM Challenge
000166759 6531_ $$aCommunity Experiment
000166759 6531_ $$aEvolutionary Robotics
000166759 700__ $$0243227$$aSchaffter, Thomas$$g161219
000166759 700__ $$aMarbach, Daniel
000166759 700__ $$0240742$$aFloreano, Dario$$g111729
000166759 773__ $$j27$$k16$$q2263-2270$$tBioinformatics
000166759 8564_ $$s812854$$uhttps://infoscience.epfl.ch/record/166759/files/Schaffter2011.pdf$$yPublisher's version$$zPublisher's version
000166759 8564_ $$s174642$$uhttps://infoscience.epfl.ch/record/166759/files/Schaffter2011_sm.pdf$$zn/a
000166759 909C0 $$0252161$$pLIS$$xU10370
000166759 909CO $$ooai:infoscience.tind.io:166759$$pSTI$$particle$$qGLOBAL_SET
000166759 917Z8 $$x161219
000166759 917Z8 $$x161219
000166759 917Z8 $$x161219
000166759 917Z8 $$x111729
000166759 917Z8 $$x128933
000166759 917Z8 $$x128933
000166759 917Z8 $$x161219
000166759 917Z8 $$x161219
000166759 917Z8 $$x161219
000166759 917Z8 $$x161219
000166759 917Z8 $$x255330
000166759 937__ $$aEPFL-ARTICLE-166759
000166759 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000166759 980__ $$aARTICLE