Reverse Engineering Literature
This page contains DREAM project derived or related papers. If you are aware of one that is not in the list, please let know at Contact us.
A list of articles published in PloS is also available on the PloS Collections web page
2013
Margolin, a. a. and Bilal, E.Huang, E. Norman, T. C. and Ottestad, L. and Mecham, B. H. and Sauerwine, B. and Kellen, M. R. and Mangravite, L. M. and Furia, M. D. and Vollan, H. K. M. and Rueda, O. M. and Guinney, J. and Deflaux, N. a. and Hoff, B. and and Schildwachter, X. and Russnes, H. G. and Park, D. and Vang, V. O. and Pirtle, T. and Youseff, L. and Citro, C. and Curtis, C. and Kristensen, V. N. and Hellerstein, J. and Friend, S. H. and Stolovitzky, G. and Aparicio, S. and Caldas, C. and Borresen-Dale, A.-L.
Systematic Analysis of Challenge-Driven Improvements in Molecular Prognostic Models for Breast Cancer.
Science Translational Medicine. 2013 (5) 181
Citations details
Cheng, W.-Y. and Yang, T.-H. O. and Anastassiou, D.
Development of a Prognostic Model for Breast Cancer Survival in an Open Challenge Environment.
Science Translational Medicine. 2013 (5) 181
Citations details
Aghaeepour N, Finak G, The FlowCAP Consortium, et al
Critical assessment of automated flow cytometry data analysis techniques.
Nat Methods. 2013 Feb 10. doi: 10.1038/nmeth.2365.
Citations details
Weirauch, M. T., Cote, A., Norel, R., Annala, M., Zhao, Y., Riley, T. R., Saez-Rodriguez, J., Cokelaer, T., Vedenko, A., Talukder, S., DREAM5 Consortium, Bussemaker H.J., Morris, Q.D., Bulyk, M,L,Stolovitsky G., Hughes, T.R.
Evaluation of methods for modeling transcription factor sequence specificity. Nature biotechnology, (Jan 2013). Citation Details
2012
Steiert, Bernhard; Raue, Andreas; Timmer, Jens; Kreutz, Clemens (2012)
Experimental design for parameter estimation of gene regulatory networks.
PloS ONE vol. 7 (7), e40052, 2012. Citations details
Ackermann, Marit and Clément-Ziza, Mathieu and Michaelson, Jacob J. and Beyer, Andreas
Teamwork: Improved eQTL Mapping Using Combinations of Machine Learning Methods.
PLoS ONE, 7(7), e40916, 2012.Citation details
Marbach, D. and Costello, J.C. and Kueffner R. and Vega, N.D. and Prill, R.J. and Camacho, D.M. and Allison, K.R. and the DREAM5 Consortium and Kellis, M. and Collins J.J. and Stolovitzky, G.
Wisdom of crowds for robust gene network inference.
Nature Methods (in press).
Kueffner, R. and Petri, T. and Tavakkolkhah, P. and Windhager, L. and Zimmer, R.
Inferring gene regulatory networks by ANOVA.
Bioinformatics, 28(10),1376-1382, 2012.
2011
Barbarini, N. and Tiengo A. and Bellazzi, R.
Prediction of peptide reactivity with human IVIg through a knowledge-based approach.
PloS one,6(8),e23616,2012. Citations Details
Prill, R.J. and Saez-rodriguez, J. and Alexopoulos, L.G. and Sorger, P.K. and Stolovitzky, G. Crowdsourcing network inference: the DREAM predictive signaling network challenge.
Science signaling,4(189),mr7,2011. Citations Details
Ellis, J.J. and Kobe, B.
Predicting protein kinase specificity: Predikin update and performance in the DREAM4 challenge.
PloS one,6(7),e21169,2011.Citations Details
Loh, P-R. Tucker, G. and Berger, B.
Phenotype Prediction Using Regularized Regression on Genetic Data in the DREAM5 Systems Genetics B Challenge
PLoS ONE,6(12),e29095,2011. Citations Details
2010
Gustafsson, M. and Hörnquist, M.
Gene expression prediction by soft integration and the elastic net-best performance of the DREAM3 gene expression challenge.
PloS one, 5(2),e9134,2010. Citations Details
Ruan, J.
A top-performing algorithm for the DREAM3 gene expression prediction challenge.
PloS one, 5(2),e8944,2010 Citations Details
Guex, N. Migliavacca, E. and Xenarios, I.
Multiple imputations applied to the DREAM3 phosphoproteomics challenge: a winning strategy.
PloS one, 5(1),e8012, 2010, Citations Details
Madar, A. and Greenfield, A. and Vanden-eijnden, E. and Bonneau, R.
DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator.
PloS ONE, 5(3),e9803,2010.Citations Details
Clarke, N.D. and Bourque, G.
Success in the DREAM3 signaling response challenge using simple weighted-average imputation: lessons for community-wide experiments in systems biology.
PloS ONE, 5(1),e8417,2010.Citations Details
Prill, R.J. and Marbach, D. Saez-rodriguez, J. and Sorger, P.K. and Alexopoulos, L.G. and Xue, X. and Clarke, N.D. and Altan-bonnet, G. and Stolovitzky, G.
Towards a rigorous assessment of systems biology models: the DREAM3 challenges.
PloS ONE, 5(2),e9202,2010. Citations Details.
Menéndez, P. and Kourmpetis, Y.A.I. and Ter Braak, C.J.F. Van Eeuwijk, F.A.
Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.
PloS ONE, 5(12),e14147,2010. Citations Details
Greenfield, Alex and Madar, Aviv and Ostrer, Harry and Bonneau, Richard,
DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models.
PloS ONE,5,(10,),e13397,2010. Citations Details
Hong, Seungpyo and Chung, Taesu and Kim, Dongsup,
SH3 domain-peptide binding energy calculations based on structural ensemble and multiple peptide templates.,
PloS ONE, 5,(9,),e12654,2010.Citations Details
Pinna, Andrea and Soranzo, Nicola and de la Fuente, Alberto,
From knockouts to networks: establishing direct cause-effect relationships through graph analysis.
PloS ONE, 5,(10,),e12912,2010. Citations Details
Huynh-Thu, Vâan Anh and Irrthum, Alexandre and Wehenkel, Louis and Geurts, Pierre,
Inferring regulatory networks from expression data using tree-based methods.
PloS ONE, 5,(9,),10,2010. Citations Details
Küuffner, Robert and Petri, Tobias and Windhager, Lukas and Zimmer, Ralf,
Petri Nets with Fuzzy Logic (PNFL): reverse engineering and parametrization.
PloS ONE, 5,(9,),10,2010. Citations Details
Zaslavsky, Elena and Bradley, Philip and Yanover, Chen,
Inferring PDZ domain multi-mutant binding preferences from single-mutant data.
PloS ONE, 5,(9,),e12787,2010.Citations Details
Yip, Kevin Y and Alexander, Roger P and Yan, Koon-Kiu and Gerstein, Mark,
Improved reconstruction of in silico gene regulatory networks by integrating knockout and perturbation data.
PloS ONE,5,(1,),e8121,2010. Citations Details
Eduati, Federica and Corradin, Alberto and Di Camillo, Barbara and Toffolo, Gianna,
A Boolean approach to linear prediction for signaling network modeling.
PloS ONE, 5,(9,),6, 2010.Citations Details
2009
Annals of the New York Academy of Sciences
Volume 1158, The Challenges of Systems Biology Community Efforts to Harness Biological Complexity,Pages ix–xii, 1–316
Stolovitzky, G. and Kahlem, P. and Califano, A. Preface. Annals of the New York Academy of Sciences,1158(1),ix--xii,2009.Citations Details.
Krallinger, M. and Rojas, A.M. and Valencia, A.
Creating Reference Datasets for Systems Biology Applications Using Text Mining
Annals of the New York Academy of Sciences,1158(1),14--28,2009. Citations Details
Adler, P. and Peterson, H. and Agius, P. and Reimand, J. and Vilo, J.
Ranking Genes by Their Co-expression to Subsets of Pathway Members
Annals of the New York Academy of Sciences,1158(1),1-13,2009.Citations Details
Lemmens, K. and De Bie, T. and Dhollander, T. and Monsieurs, P. and De Moor, B. and Collado-Vides, J. and Engelen, K. and Marchal, K.
The Condition-Dependent Transcriptional Network in Escherichia coli
Annals of the New York Academy of Sciences,1158(1),29--35,2009. Citations Details
Michoel, T. and De Smet, R. and Joshi, A. and Marchal, K. and de Peer, Y.
Reverse-Engineering Transcriptional Modules from Gene Expression Data
Annals of the New York Academy of Sciences,1158(1),36--43,2009.Citations Details
Lipshtat, A. and Neves, S. R and Iyengar, R.
Specification of Spatial Relationships in Directed Graphs of Cell Signaling Networks
Annals of the New York Academy of Sciences,1158(1),44--56,2009.Citations Details
Hoffmann, S. and Holzhutter, H.G.
Uncovering Metabolic Objectives Pursued by Changes of Enzyme Levels
Annals of the New York Academy of Sciences,1158(1),57--70,2009.Citations Details
Gowda, T. and Vrudhula, S. and Kim, S.
Modeling of Gene Regulatory Network Dynamics Using Threshold Logic
Annals of the New York Academy of Sciences,1158(1),71--81,2009.Citations Details
Gong, Y. and Zhang, Z.
Global Robustness and Identifiability of Random, Scale-Free, and Small-World Networks
Annals of the New York Academy of Sciences,1158(1),82--92,2009.Citations Details
Yoo, C. and Brilz, E. M.
The Five-Gene-Network Data Analysis with Local Causal Discovery Algorithm Using Causal Bayesian Networks
Annals of the New York Academy of Sciences,1158(1),93--101,2009.Citations Details
Marbach, D. and Mattiussi, C. and Floreano, D.
Combining Multiple Results of a Reverse-Engineering Algorithm: Application to the DREAM Five-Gene Network Challenge
Annals of the New York Academy of Sciences,1158(1),102--113,2009.Citations Details
Parisi, F. and Koeppl, H. and Naef, F.
Network Inference by Combining Biologically Motivated Regulatory Constraints with Penalized Regression
Annals of the New York Academy of Sciences,1158(1),114--124,2009.Citations Details
Di Camillo, B. and Toffolo, G. and Cobelli, C.
A Gene Network Simulator to Assess Reverse Engineering Algorithms
Annals of the New York Academy of Sciences,1158(1),125--142,2009.Citations Details
Taylor, R. C. and Singhal, M. and Weller, J. and Khoshnevis, S. and Shi, L. and McDermott, J.
A Network Inference Workflow Applied to Virulence-Related Processes in Salmonella typhimurium
Annals of the New York Academy of Sciences,1158(1),143--158,2009.Citations Details
Stolovitzky, G and Prill, R. J and Califano, A.
Lessons from the DREAM2 Challenges.
Annals Of The New York Academy Of Sciences,1158(1),159--195,2009.Citations Details
Lee, W. H. and Narang, V. and Xu, H. and Lin, F. and Chin, K. C. and Sung, W. K.
DREAM2 Challenge
Annals of the New York Academy of Sciences,1158(1),196--204,2009.Citations Details
Nykter, M. and Lahdesmaki, H. and Rust, A. and Thorsson, V. and Shmulevich, I.
A Data Integration Framework for Prediction of Transcription Factor Targets
Annals of the New York Academy of Sciences,1158(1),205--214,2009.Citations Details
Vega, V.B. and Woo, X.Y. and Hamidi, H. and Yeo, H. C. and Yeo, Z. X. and Bourque, G. and Clarke, N.D.
Inferring Direct Regulatory Targets of a Transcription Factor in the DREAM2 Challenge
Annals of the New York Academy of Sciences,1158(1),215--223,2009.Citations Details
Chua, H.N. and Hugo, W. and Liu, G. and Li, X. and Wong, L. and Ng, S-K
A Probabilistic Graph-Theoretic Approach to Integrate Multiple Predictions for the Protein–Protein Subnetwork Prediction Challenge
Annals of the New York Academy of Sciences,1158(1),224--233,2009.Citations Details
Marbach, D. and Mattiussi, C. and Floreano, D.
Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene Networks
Annals of the New York Academy of Sciences,1158(1),234--245,2009.Citations Details
Baralla, A. and Mentzen, W. I. and De La Fuente, A.
Inferring Gene Networks: Dream or Nightmare?
Annals of the New York Academy of Sciences,1158(1),246--256,2009.Citations Details
Lauria, M. and Iorio, F. and Di Bernardo, D.
NIRest: A Tool for Gene Network and Mode of Action Inference
Annals of the New York Academy of Sciences,1158(1),257--264,2009.Citations Details
Gustafsson, M. and Hörnquist, M. and Lundström, J. and Björkegren, J. and Tegnér, Jesper
Reverse Engineering of Gene Networks with LASSO and Nonlinear Basis Functions
Annals of the New York Academy of Sciences,1158(1),265--275,2009.Citations Details
Gowda, T. and Vrudhula, S. and Kim, S.
Prediction of Pairwise Gene Interaction Using Threshold Logic
Annals of the New York Academy of Sciences,1158(1),276--286,2009.Citations Details
Scheinine, A. and Mentzen, W. I. and Fotia, G. and Pieroni, E. and Maggio, F. and Mancosu, G. and De La Fuente, A.
Inferring Gene Networks: Dream or Nightmare?
Annals of the New York Academy of Sciences,1158(1),287--301,2009.Citations Details
Watkinson, J. and Liang, K-C and Wang, X. and Zheng, T. and Anastassiou, D.
Inference of Regulatory Gene Interactions from Expression Data Using Three-Way Mutual Information
Annals of the New York Academy of Sciences,1158(1),302--313,2009.Citations Details
Others
Bhadra, S. and Bhattacharyya, C. and Chandra, N.R. and Mian, I.S.
A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data.
Algorithms for Molecular Biology,24; 4:5, Feb. 2009.Citations Details
2008
2007
Annals of the New York Academy of Sciences
Volume 1115, The Challenges of Systems Biology Community Efforts to Harness Biological Complexity,Pages ix–xii, 1–316
Stolovitzky, G. and Monroe, D. and Califano, A.
Dialogue on Reverse-Engineering Assessment and Methods.
Annals of the New York Academy of Sciences,1115(1),1--22,2007. Citations Details
Stolovitzky, G. and Califano, A.
Preface
Annals of the New York Academy of Sciences,1115(1),xi----xiv,2007.Citations Details