Dialogue for Reverse Engineering Assessments and Methods
DREAM (Dialogue for Reverse Engineering Assessments and Methods) poses fundamental questions about systems biology, and invites participants to propose solutions. The main objective is to catalyze the interaction between theory and experiment, specifically in the area of cellular network inference and quantitative model building. DREAM challenges address how we can assess the quality of our descriptions of networks that underlie biological systems, and of our predictions of the outcomes of novel experiments. These are not simple questions. Researchers have used a variety of algorithms to deduce the structure of biological networks and/or to predict the outcome of perturbations to their systems. They have also evaluated the success of their methodologies using a diverse set of non-standardised metrics. What is still needed, and what DREAM aims to achieve, is a fair comparison of the strengths and weaknesses of these methods and a clear sense of the reliability of the models that researchers create.
On June 2, 2014, DREAM and Sage Bionetworks opened three DREAM Challenges: the delayed Alzheimer’s Disease Big Data DREAM Challenge #1 along with two DREAM #9 Challenges: The Broad-DREAM Gene Essentiality Prediction Challenge, and the The DREAM9 Acute Myeloid Leukemia (AML) Outcome Prediction Challenge.
These Challenges tackle important questions related to Alzheimer’s Disease, cancer genomics and acute myeloid leukemia (AML). Help us journey forward as we strive to maintain DREAM’s high level of excellence in translational systems biology while also welcoming new DREAMers to our community who can help us innovate and extend the wisdom of the crowds into new arenas of human health.
This Challenge season will conclude with final Challenge submissions due in mid-September, 2014. Best performers from the Alzheimer’s Disease Big Data DREAM Challenge will be invited to present their results at the International Biomedical Commons Congress, to be held in Paris in April 2015. Best performers from the other two Challenges will be invited to present their results at the DREAM track of the RECOMB/ISCB Systems and Regulatory Genomics/DREAM Conference, to be held in San Diego, California November 10-14, 2014.
As we have done in previous Challenges, best performer teams will also be invited to co-author a Challenge-specific paper for submission to a scientific journal
Alzheimer’s Disease Big Data DREAM Challenge #1
Predict the best biomarkers for early AD-related cognitive decline and for the mismatch between high amyloid levels and cognitive decline.
The Broad-DREAM Gene Essentiality Prediction Challenge
Develop predictive models to infer genes that are essential to cancer cell viability using gene expression and/or gene copy number features.
The DREAM9 Acute Myeloid Leukemia (AML) Outcome Prediction Challenge
Predict the outcome of treatment of AML patients (resistant or remission), their remission duration and overall survival based on clinical cytogentics, known genetics markers and phosphoproteomic data.
Ongoing DREAM8.5 Challenges
ICGC-TCGA-DREAM Somatic Mutation Calling Challenge (Closes 8/14/14)
Predict cancer-associated mutations from whole-genome sequencing data.
The Rheumatoid Arthritis Responder Challenge (Closes 6/4/14)
Predict which Rheumatoid Arthritis patients will not respond to anti-TNF therapy.
Final results of the 2013 DREAM8 Challenges
We are pleased to announce the final results for the DREAM 8 Challenges. During the "Challenge season" that spanned June 10, 2013 to mid-September, 2013, Sage Bionetworks and DREAM ran the three DREAM8 Challenges described below. More than 600 individuals signed up for the Challenges and in aggregate built and submitted >1000 predictive models across the three Challenges in a timespan of roughly three months. The top performing teams presented their winning models at the November 8, 2013 DREAM Conference. Click here to see the winning teams and other DREAM conference photos.
DREAM 2 through 8
To view challenges of years before 2013 please please click in the Challenges tab.
How to cite DREAM
If you use a DREAM data set in your publication, please cite both the main DREAM publication for the Conference and the publication of the data producer, which you can find in the Challenges section of this web site.
The main DREAM publications are:
- on a DREAM5 (2010) challenge:Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, The DREAM5 Consortium, Kellis M, Collins JJ, Stolovitzky G., Wisdom of crowds for robust gene network inference, Nature Methods, 2012, in press.Advanced online publication, 15 July 2012.
- on a DREAM4 (2009) challenge:Prill RJ, Saez-Rodriguez J, Alexopoulos LG, Sorger PK, Stolovitzky G., Crowdsourcing Network Inference: The DREAM4 Predictive Signaling Network Challenge,Science Signaling 2011 Sep 6;4(189):mr7.
- on a DREAM3 (2008) challenge: 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.
- DREAM3 (2008): Prill RJ, Marbach D, Saez-Rodriguez J, SorgerPK, Alexopoulos LG, Xue X, Clarke ND, Altan-Bonnet G, and Stolovitzky G. Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS One, 5(2):e9202, 2010. [ Read at PLoS ONE ]
- DREAM2 (2007): Stolovitzky G, Prill RJ, Califano A. "Lessons from the DREAM2 Challenges", in Stolovitzky G, Kahlem P, Califano A, Eds, Annals of the New York Academy of Sciences, 1158:159-95 (2009)
- DREAM1 (2006): Stolovitzky G, Monroe D, Califano A. "Dialogue on Reverse-Engineering Assessment and Methods: The DREAM of High-Throughput Pathway Inference", in Stolovitzky G and Califano A, Eds, Annals of the New York Academy of Sciences, 1115:11-22 (2007)
- Gustavo Stolovitzky, IBM Computational Biology Center
- Andrea Califano, Columbia University
If you would like to design a challenge, or donate unpublished data to be the basis of a challenge, please contact Gustavo Stolovitzky.
- Columbia University Center for Multiscale Analysis Genomic and Cellular Networks
- IBM Computational Biology Center
- The New York Academy of Sciences
- NIH Roadmap Initiative
- Sage Bionetworks