DREAM Publications
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.