Reverse Engineering Literature

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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.

This page contains DREAM project derived (related?)  papers. If you are aware of one that is not in the list, please let us know at  XXX 

 

A list of articles published in PloS is also available on the PloS Collections web page

2014

James C Costello et al., A community effort to assess and improve drug sensitivity prediction algorithms Nature BioTechnology, 2014

Meyer, Pablo;  Cokelaer, Thomas;  Chandran, Deepak;  Kim, Kyung Hyuk;  Loh, Po-Ru et al.
Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach BMC Systems Biology vol. 8 (1) p. 13

 2013

Robert M Plenge, Jeffrey D Greenberg, Lara M Mangravite, Jonathan M J Derry, Eli A Stahl, Marieke J H Coenen, Anne Barton, Leonid Padyukov, Lars Klareskog, Peter K Gregersen, Xavier Mariette, Larry W Moreland, S Louis Bridges Jr, Niek de Vries, Tom W J Huizinga, Henk-Jan Guchelaar, International Rheumatoid Arthritis Consortium (INTERACT), Stephen H Friend & Gustavo Stolovitzky
Crowdsourcing genetic prediction of clinical utility in the Rheumatoid Arthritis Responder Challenge
Nature Genetics 45,468-469 (2013) doi:10.1038/ng.2623

Bilal E, Dutkowski J, Guinney J, Jang IS, Logsdon BA, et al. (2013)
Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling.
PLoS Comput Biol 9(5): e1003047. doi:10.1371/journal.pcbi.1003047

Pablo Meyer, Geoffrey Siwo, Danny Zeevi, Eilon Sharon, Raquel Norel, DREAM6 Promoter Prediction Consortium, Eran Segal and Gustavo Stolovitzky
Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach
Genome Res. November 201323: 1928-1937                                      

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

Biehl M, Bunte K, Schneider P (2013)
Analysis of Flow Cytometry Data by Matrix Relevance Learning Vector Quantization.
PLoS ONE 8(3): e59401. doi:10.1371/journal.pone.0059401

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


 

R. J. Flassig, S. Heise, K. Sundmacher and S. Klamt
An effective framework for reconstructing gene regulatory networks from genetical genomics data
Bioinformatics (2013) 29 (2): 246-254.doi: 10.1093/bioinformatics/bts679


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

Cover image for Vol. 1158 The Challenges of Systems Biology Community Efforts to Harness Biological Complexity

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

image

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