@article {814,
	title = {Wisdom of crowds for robust gene network inference},
	journal = {Nature Methods},
	volume = {9},
	number = {8},
	year = {2012},
	pages = {796-804},
	doi = {10.1038/nmeth.2016},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870305264\&partnerID=40\&md5=04a686572bdefff60157bf68c95df7ea},
	author = {Daniel Marbach and J.C. Costello and Robert K{\"u}ffner and N.M. Vega and R.J. Prill and D.M. Camacho and K.R. Allison and Manolis Kellis and J.J. Collins and Aderhold, A. and Gustavo Stolovitzky and Bonneau, R. and Chen, Y. and Cordero, F. and Martin Crane and Dondelinger, F. and Drton, M. and Esposito, R. and Foygel, R. and De La Fuente, A. and Gertheiss, J. and Geurts, P. and Greenfield, A. and Grzegorczyk, M. and Haury, A.-C. and Holmes, B. and Hothorn, T. and Husmeier, D. and Huynh-Thu, V.A. and Irrthum, A. and Karlebach, G. and Lebre, S. and De Leo, V. and Madar, A. and Mani, S. and Mordelet, F. and Ostrer, H. and Ouyang, Z. and Pandya, R. and Petri, T. and Pinna, A. and Poultney, C.S. and Rezny, S. and Heather J Ruskin and Saeys, Y. and Shamir, R. and Alina Sirbu and Song, M. and Soranzo, N. and Statnikov, A. and N.M. Vega and Vera-Licona, P. and Vert, J.-P. and Visconti, A. and Haizhou Wang and Wehenkel, L. and Windhager, L. and Zhang, Y. and Zimmer, R.}
}
@article {797,
	title = {Wisdom of crowds for robust gene network inference.},
	journal = {Nat Methods},
	volume = {9},
	year = {2012},
	month = {2012 Aug},
	pages = {796-804},
	abstract = {<p>Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50\%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43\%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.</p>},
	issn = {1548-7105},
	doi = {10.1038/nmeth.2016},
	author = {Daniel Marbach and J.C. Costello and Robert K{\"u}ffner and N.M. Vega and R.J. Prill and D.M. Camacho and K.R. Allison and Manolis Kellis and J.J. Collins and Gustavo Stolovitzky}
}
