TY - JOUR
T1 - Wisdom of crowds for robust gene network inference
JF - Nature Methods
Y1 - 2012
A1 - Daniel Marbach
A1 - J.C. Costello
A1 - Robert Küffner
A1 - N.M. Vega
A1 - R.J. Prill
A1 - D.M. Camacho
A1 - K.R. Allison
A1 - Manolis Kellis
A1 - J.J. Collins
A1 - Aderhold, A.
A1 - Gustavo Stolovitzky
A1 - Bonneau, R.
A1 - Chen, Y.
A1 - Cordero, F.
A1 - Martin Crane
A1 - Dondelinger, F.
A1 - Drton, M.
A1 - Esposito, R.
A1 - Foygel, R.
A1 - De La Fuente, A.
A1 - Gertheiss, J.
A1 - Geurts, P.
A1 - Greenfield, A.
A1 - Grzegorczyk, M.
A1 - Haury, A.-C.
A1 - Holmes, B.
A1 - Hothorn, T.
A1 - Husmeier, D.
A1 - Huynh-Thu, V.A.
A1 - Irrthum, A.
A1 - Karlebach, G.
A1 - Lebre, S.
A1 - De Leo, V.
A1 - Madar, A.
A1 - Mani, S.
A1 - Mordelet, F.
A1 - Ostrer, H.
A1 - Ouyang, Z.
A1 - Pandya, R.
A1 - Petri, T.
A1 - Pinna, A.
A1 - Poultney, C.S.
A1 - Rezny, S.
A1 - Heather J Ruskin
A1 - Saeys, Y.
A1 - Shamir, R.
A1 - Alina Sirbu
A1 - Song, M.
A1 - Soranzo, N.
A1 - Statnikov, A.
A1 - N.M. Vega
A1 - Vera-Licona, P.
A1 - Vert, J.-P.
A1 - Visconti, A.
A1 - Haizhou Wang
A1 - Wehenkel, L.
A1 - Windhager, L.
A1 - Zhang, Y.
A1 - Zimmer, R.
VL - 9
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84870305264&partnerID=40&md5=04a686572bdefff60157bf68c95df7ea
ER -
TY - JOUR
T1 - Wisdom of crowds for robust gene network inference.
JF - Nat Methods
Y1 - 2012
A1 - Daniel Marbach
A1 - J.C. Costello
A1 - Robert Küffner
A1 - N.M. Vega
A1 - R.J. Prill
A1 - D.M. Camacho
A1 - K.R. Allison
A1 - Manolis Kellis
A1 - J.J. Collins
A1 - Gustavo Stolovitzky
AB - 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.

VL - 9
ER -