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 these questions to the whole scientific community as a collaborative competition whereby, together with specific data providers, we pose questions and provide the data necessary to address them, such as what is the best drug to treat a tumour type. Anyone can participate, and DREAM provides an unbiased, rigorous assessment of a team’s solution. When the challenge is closed, participants’ solutions are analyse to learn what methods did well.
Julio is DREAM co-Director for Computational Systems Biology Challenges, and we have been involved in a number of challenges.
See below a video about a recent DREAM challenge (Eduati et al., Nat Biotechnol. 2015) on toxicogenomics:
- Crowdsourcing biomedical research: leveraging communities as innovation engines. Saez-Rodriguez J, et al. Nat Rev Genet 17 (2016) p. 470-486
Inferring causal molecular networks: empirical assessment through a community-based effort. Hill SM, Heiser LM, et al. Nat Methods Volume 13 (2016) p. 310-318
Prediction of human population responses to toxic compounds by a collaborative competition. Eduati F, et al. Nat Biotechnol 33 (2015) p. 933-940
A community effort to assess and improve drug sensitivity prediction algorithms. Costello JC, et al. Nat Biotechnol 32 (2014) p. 1202-1212
Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach. Meyer P, et al. BMC Syst Biol 8 (2014) p. 13
Crowdsourcing network inference: the DREAM predictive signaling network challenge. Prill RJ, et al. Sci Signal 4 (2011) p. mr7