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Table of Contents: The DREAM systems biology challenges: a Dialogue for Reverse Engineering Assessment and Methods

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This Collection contains papers representing the output of the best performing methods in the DREAM challenges, which themselves were discussed and presented at the DREAM (Dialogue for Reverse Engineering Assessment and Methods) conferences, which have been running annually since 2007.

Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets; however, the volume of data being generated is accelerating as molecular profiling technology evolves. Computational algorithms play a prominent role in the interpretation of systems biology data and so the Dialogue for Reverse Engineering Assessments and Methods (DREAM) grew as a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual conferences and reverse-engineering and predictive modeling challenges. The various challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. The articles in this Collection describe the methods and strategies that achieved best performance since the DREAM challenges of 2008 (the DREAM3 challenges) and onwards (DREAM4, DREAM5, etc.). These challenges encompass a variety of problems at the core of systems biology, such as network inference, determination of diagnostic signatures, prediction of outcomes of biological systems as a result of perturbations, transcriptional dynamics, protein-protein recognition, etc. The Collection as a whole summarizes the lessons learned by the community in the DREAM challenges and provides a much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature. Articles are presented in order of publication date and new articles will be added to the Collection as they are published.

The DREAM project is sponsored by NCI through its Columbia University Center for Multiscale Analysis Genomic and Cellular Networks (MAGNet) and by the IBM Computational Biology Center.

Image Credit: Gustavo Stolovitzky

Overview

Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

Robert J. Prill, Daniel Marbach, Julio Saez-Rodriguez, Peter K. Sorger, Leonidas G. Alexopoulos, Xiaowei Xue, Neil D. Clarke, Gregoire Altan-Bonnet, Gustavo Stolovitzky

Research Articles

Leukemia Prediction Using Sparse Logistic Regression

Tapio Manninen, Heikki Huttunen, Pekka Ruusuvuori, Matti Nykter

Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

Michael P. Menden, Francesco Iorio, Mathew Garnett, Ultan McDermott, Cyril H. Benes, Pedro J. Ballester, Julio Saez-Rodriguez

Teamwork: Improved eQTL Mapping Using Combinations of Machine Learning Methods

Marit Ackermann, Mathieu Clément-Ziza, Jacob J. Michaelson, Andreas Beyer

Experimental Design for Parameter Estimation of Gene Regulatory Networks

Bernhard Steiert, Andreas Raue, Jens Timmer, Clemens Kreutz

Prediction of Peptide Reactivity with Human IVIg through a Knowledge-Based Approach

Nicola Barbarini, Alessandra Tiengo, Riccardo Bellazzi

Gene Regulatory Networks from Multifactorial Perturbations Using Graphical Lasso: Application to the DREAM4 Challenge

Patricia Menéndez, Yiannis A. I. Kourmpetis, Cajo J. F. ter Braak, Fred A. van Eeuwijk

Inferring Regulatory Networks from Expression Data Using Tree-Based Methods

Vân Anh Huynh-Thu, Alexandre Irrthum, Louis Wehenkel, Pierre Geurts

Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization

Robert Küffner, Tobias Petri, Lukas Windhager, Ralf Zimmer

A Boolean Approach to Linear Prediction for Signaling Network Modeling

Federica Eduati, Alberto Corradin, Barbara Di Camillo, Gianna Toffolo

DREAM3: Network Inference Using Dynamic Context Likelihood of Relatedness and the Inferelator

Aviv Madar, Alex Greenfield, Eric Vanden-Eijnden, Richard Bonneau