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October 29, 2011

DREAM6 – 2011 All Stars

DREAM6 Alternative Splicing

Team TeamTrinity – Manfred Grabherr, Brian Haas, Moran Yassour, Michael Ott, Nathalie Pochet, Nir Friedman and Aviv Regev

Team orangeballs – Ramya Rangan, Po-Ru Loh and Bonnie Berger

Team ALF – Massimiliano Orsini and Alberto de la Fuente

DREAM6 Estimation of Model Parameters

Team orangeballs – Ramya Rangan, Po-Ru Loh and Bonnie Berger

Team crux – Clemens Kreutz, Andreas Raue , Berny Steiert and Jens Timmer

DREAM6 Gene Expression Prediction Challenge

Team FIrST – Andrew Rider, Asako Tan, Richard Pinapati and Geoffrey Siwo

Team c4lab of Yi-An Tung*, Yong-Syuan Chen*, Mei-Ju May Chen, and Chien-Yu Chen (*equal contribution)

Team Blue1 – Daphne Ezer, Yezhou Huang, Fantine Mordelet and Alex Hartemink

DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge

Team team21 – Jose Vilar

Team BCB – Peng Qiu

Team Admire-LVQ – Michael Biehl,   Kerstin Bunte and   Petra Schneider

Team JKJG – Jens Keilwagen

Team Daltons– Tapio Manninen, Heikki Huttunen, Pekka Ruusuvuori and Matti Nykter

Team ACGT – Yaron Orenstein, David Amar, Ron Zeira and Ron Shamir

Team biolobe – Marc Strickert and Michael Seifert

Team SCRPH1 – Wei Keat Lim

October 29, 2011

2011 Publications

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. doi:10.1371/journal.pone.0023616

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
Sci Signal. 2011 Aug 30;4(189):mr7. doi:10.1126/scisignal.2002212

Ellis, J.J. and Kobe, B.
Predicting protein kinase specificity: Predikin update and performance in the DREAM4 challenge
PLoS ONE 6(7): e21169. doi:10.1371/journal.pone.0021169

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. doi:10.1371/journal.pone.0029095

Schaffter T1, Marbach D, Floreano D.
GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods.
Bioinformatics. 2011 Aug 15;27(16):2263-70. doi: 10.1093/bioinformatics/btr373. Epub 2011 Jun 22.

 

October 26, 2011

DREAM6 Conference

Oct 14 2011, Barcelona

Conference to discuss the results of the DREAM6 challenges

October 29, 2010

DREAM5 – 2010 All Stars

Epitope-Antibody Recognition (EAR) Challenge

Team Pythia – Rob Patro and Carl Kingsford

Team Pavia_Working_Group – Nicola Barbarini, Alessandra Tiengo and Riccardo Bellazzi

TF-DNA Motif Recognition Challenge

Team csb_tut– Matti Annala, Kirsti Laurila, Matti Nykter, and Harri Lähdesmäki

Team ACGT – Yaron Orenstein, Chaim Linhart and Ron Shamir

Team EPD – Philipp Bucher

Team JGJK – Jan Grau, Ivo Grosse, Stefan Posch and Jens Keilwagen

Systems Genetics Challenge

Part A

Team SaAB_meta, SaAb Dantzig – M. Vignes, J. Vandel, N. Ramadan, D. Allouche, C. Cierco, S. de Givry, B. Mangin and T. Schiex

Team ulg_biomod – Vân Anh Huynh-Thu , Alexandre Irrthum , Louis Wehenkel, Yvan Saeys and Pierre Geurts

Team GIANO5 – Tiziana Sanavia, Francesco Sambo, Angela Grassi, Barbara Di Camillo and Gianna Toffolo

Part B

Team orangeballs – Po-Ru Loh, George Tucker, Michael Yu, and Bonnie Berger

Part B3

Team RNI_group – Madhuchhanda Bhattacharjee and Mikko J. Sillanpää

Network Inference Challenge

Team ulg_biomod – Vân Anh Huynh-Thu, Alexandre Irrthum , Louis Wehenkel, Yvan Saeys and Pierre Geurts

Team amalia – Robert Küffner, Tobias Petri, Lukas Windhager and Ralf Zimmer

Team ALF – Andrea Pinna, Nicola Soranzo, Vincenzo De Leo and Alberto de la Fuente

Team paulthepoulp – Anne-Claire Haury, Paola Vera-Licona, Fantine Mordelet and Jean-Philippe Vert

October 29, 2010

2010 Publications

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. doi:10.1371/journal.pone.0009134

Ruan, J.
A top-performing algorithm for the DREAM3 gene expression prediction challenge
PLoS ONE 5(2): e8944. doi:10.1371/journal.pone.0008944

Guex, N. Migliavacca, E. and Xenarios, I.
Multiple imputations applied to the DREAM3 phosphoproteomics challenge: a winning strategy
PLoS ONE 5(1): e8012. doi:10.1371/journal.pone.0008012

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. doi:10.1371/journal.pone.0009803

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. doi:10.1371/journal.pone.0008417

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. doi:10.1371/journal.pone.0009202

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. doi:10.1371/journal.pone.0014147

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. doi:10.1371/journal.pone.0013397

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. doi:10.1371/journal.pone.0012654

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. doi:10.1371/journal.pone.0012912

Huynh-Thu, Vân Anh and Irrthum, Alexandre and Wehenkel, Louis and Geurts, Pierre,
Inferring regulatory networks from expression data using tree-based methods
PLoS ONE 5(9): e12776. doi:10.1371/journal.pone.0012776

Küffner, Robert and Petri, Tobias and Windhager, Lukas and Zimmer, Ralf,
Petri Nets with Fuzzy Logic (PNFL): reverse engineering and parameterization
PLoS ONE 5(9): e12807. doi:10.1371/journal.pone.0012807

Zaslavsky, Elena and Bradley, Philip and Yanover, Chen,
Inferring PDZ domain multi-mutant binding preferences from single-mutant data
PLoS ONE 5(9): e12787. doi:10.1371/journal.pone.0012787

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. doi:10.1371/journal.pone.0008121

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): e12789. doi:10.1371/journal.pone.0012789

Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, Stolovitzky G.
Revealing strengths and weaknesses of methods for gene network inference.
Proc Natl Acad Sci U S A. 2010 Apr 6;107(14):6286-91. doi: 10.1073/pnas.0913357107. Epub 2010 Mar 22.

 

 

October 29, 2009

DREAM4 – 2009 All Stars

Peptide Recognition Domain (PRD) Specificity Prediction

PDZ Domains

Team Chuck_Daly – Philip Bradley, Chen Yanover and Elena Zaslavsky

SH3 Domains

Team PBIL- Taesu Chung, Seungpyo Hong and Dongsup Kim

Kinase Domains

Team Predikin- Jonathan J Ellis, Bostjan Kobe and Neil FW Saunders

In Silico Network Challenge

Networks of Size 100

Team ALF – Alberto de la Fuente, Andrea Pinna and Nicola Soranzo

Team Bonneau – Richard Bonneau, Alex Greenfield, Aviv Madar and Harry Ostrer

Networks of Size 10

Team amalia – Florian Erhard, Robert Küffner, Tobias Petri, Lukas Windhager, Ralf Zimmer

Networks of Size 100 with multi-factorial perturbations

Team ulg_biomod– Pierre Geurts, Vân Anh Huynh-Thu, A Irrthum and Louis Wehenkel

Predictive Signaling Network Modeling

Team Giano4 – Albero Corradin, Barbara Di Camillo, Federica Eduati and Gianna Toffolo

Team Steam– John Schwacke

October 29, 2009

2009 Publications

Annals of the New York Academy of Sciences Volume 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: ix–xii. doi:10.1111/j.1749-6632.2009.04470.x

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: 14–28. doi:10.1111/j.1749-6632.2008.03750.x

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: 1–13. doi:10.1111/j.1749-6632.2008.03747.x

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: 29–35. doi:10.1111/j.1749-6632.2008.03746.x

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: 36–43. doi:10.1111/j.1749-6632.2008.03943.x

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: 44–56. doi:10.1111/j.1749-6632.2008.03748.x

Hoffmann, S. and Holzhutter, H.G.
Uncovering Metabolic Objectives Pursued by Changes of Enzyme Levels
Annals of the New York Academy of Sciences, 1158: 57–70. doi:10.1111/j.1749-6632.2008.03753.x

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: 71–81. doi:10.1111/j.1749-6632.2008.03754.x

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: 82–92. doi:10.1111/j.1749-6632.2008.03752.x

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: 93–101. doi:10.1111/j.1749-6632.2008.03749.x

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: 102–113. doi:10.1111/j.1749-6632.2008.03945.x

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: 114–124. doi:10.1111/j.1749-6632.2008.03751.x

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: 125–142. doi:10.1111/j.1749-6632.2008.03756.x

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: 143–158. doi:10.1111/j.1749-6632.2008.03762.x

Stolovitzky, G and Prill, R. J and Califano, A.
Lessons from the DREAM2 Challenges
Annals of the New York Academy of Sciences, 1158: 159–195. doi:10.1111/j.1749-6632.2009.04497.x

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: 196–204. doi:10.1111/j.1749-6632.2008.03755.x

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: 205–214. doi:10.1111/j.1749-6632.2008.03758.x

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: 215–223. doi:10.1111/j.1749-6632.2008.03759.x

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: 224–233. doi:10.1111/j.1749-6632.2008.03760.x

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: 234–245. doi:10.1111/j.1749-6632.2008.03944.x

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: 246–256. doi:10.1111/j.1749-6632.2008.04099.x

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: 257–264. doi:10.1111/j.1749-6632.2008.03761.x

Gustafsson, M., Hörnquist M,  Lundström J, Björkegren J, and Tegnér J.
Reverse Engineering of Gene Networks with LASSO and Nonlinear Basis Functions
Annals of the New York Academy of Sciences, 1158: 265–275. doi:10.1111/j.1749-6632.2008.03764.x

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: 276–286. doi:10.1111/j.1749-6632.2008.03763.x

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: 287–301. doi:10.1111/j.1749-6632.2008.04100.x

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: 302–313. doi:10.1111/j.1749-6632.2008.03757.x

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 2009, 4:5. doi:10.1186/1748-7188-4-5

Marbach D, Schaffter T, Mattiussi C, Floreano D.
Generating realistic in silico gene networks for performance assessment of reverse engineering methods.
J Comput Biol. 2009 Feb;16(2):229-39. doi: 10.1089/cmb.2008.09TT.

 

October 29, 2008

DREAM3 – 2008 All Stars

Signaling Response Prediction

Phosphoprotein and Cytokine subchallenges

Team Genome Singapore – Guillaume Bourque and Neil Clarke

Phosphoprotein subchallenge

Team Vital SIB – Nicolas Guex, Eugenia Migliavacca, and Ioannis Xenarios

Gene Expression Prediction

Team Gustafsson-Hornquist – Mika Gustafsson and Michael Hornquist

Team Dream Team 2008 – Jianhua Ruan

In Silico Network Challenge

Overall

Team B Team– Kevin Y. Yip, Roger P. Alexander, Koon-Kiu Yan, and Mark Gerstein

10-node & 50-node

Team USMtec347– Peng Li and Chaoyang Zhang

100-node

Team Bonneau – Aviv Madar,Alex Greenfield, Eric Vanden-Eijnden, and Richard Bonneau

Team Intigern HSP – Xuebing Wu, Feng Zeng, and Rui Jiang