Saez-Rodriguez Group

Systems Biomedicine

Crowdsourcing computational biomedicine – DREAM challenges

The main challenges in systems biomedicine are very complex, and can  not be fully solved by a research group alone. Therefore, we are involved in DREAM challenges, a community effort to advance our understanding of fundamental problems in systems biology and translational medicine.

We ask important biomedical questions to the whole scientific community as a collaborative competition whereby, together with specific data providers, we provide the data necessary to build the computational models to address them. Anyone can participate, and DREAM provides an unbiased, rigorous assessment of a team’s solution. When the challenge is closed, participants’  solutions are analysed to learn what methods did well [1].

DREAM was founded in 2006 by Gustavo Stolovitzky ( IBM Research) and Andrea Califano (Columbia University). Julio is DREAM co-Director for Computational Systems Biology Challenges, and we have been involved in a number of challenges, ranging from infererence of signaling networks from phosphoproteomic data [2] to predicting the efficacy or side effects of drugs alone or in combination [3–5], to predict (phospho)proteomic data from genomic and transcriptomic data [6], to to how to map single cells to their location in a tissue[7].  DREAM challenges can also be run on confidential data by using containerized systems to submit the models to the data, rather than releasing the data [8]

See below a video about a DREAM challenge [4] on toxicogenomics:


  1. Saez-Rodriguez J, Costello JC, Friend SH, Kellen MR, Mangravite L, Meyer P, et al. Crowdsourcing biomedical research: leveraging communities as innovation engines. Nat Rev Genet. 2016;17: 470–486.
  2. Hill SM, Heiser LM, Cokelaer T, Unger M, Nesser NK, Carlin DE, et al. Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods. 2016;13: 310–318.
  3. Costello JC, Heiser LM, Georgii E, Gönen M, Menden MP, Wang NJ, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol. 2014;32: 1202–1212.
  4. Eduati F, Mangravite LM, Wang T, Tang H, Bare JC, Huang R, et al. Prediction of human population responses to toxic compounds by a collaborative competition. Nat Biotechnol. 2015;33: 933–940.
  5. Menden MP, Wang D, Guan Y, Mason M, Szalai B, Bulusu KC, et al. A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction. 2017. doi:10.1101/200451
  6. Yang M, Petralia F, Li Z, Li H, Ma W, Song X, et al. Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics. Cell Syst. 2020;11: 186–195.e9.
  7. Tanevski J, Nguyen T, Truong B, Karaiskos N, Ahsen ME, Zhang X, et al. Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data. Life science alliance. 2020;3. Available:
  8. Guinney J, Saez-Rodriguez J. Alternative models for sharing confidential biomedical data. Nat Biotechnol. 2018;36: 391–392.