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.
2012
2011
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
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