DREAM Challenges
Solving Problems. Together
The DREAM Challenges are crowd-sourcing to solve complex biomedical research questions.
Together, we share a vision to enable individuals and groups to collaborate openly so that the 'wisdom of the crowd' provides the greatest impact on science and human health. Over sixty crowd-sourced DREAM Challenges have benchmarked informatic algorithms in biomedicine. DREAM has had over 30,000 cross-disciplinary participants from around the world that have volunteered as solvers. Over 105 academic journal publications have resulted from DREAM Challenges covering a range of disease areas.
Learn more about the DREAM process - Pose > Prepare > Engage > Evaluate > Share
Recent advances in predictive methods and availability of perceptual data have paved the way for a growing interest in olfactory perception predictions from chemical representations of molecules. This has led to a growing consensus that for pure odors, it is possible to build models using the chemical structure of molecules to predict the perceptual values of natural language attributes of smells. However, predictions have mainly focused on pure molecules and not the real-world situation of olfactory mixtures. In order to start filling this gap, we plan to organize a second DREAM olfaction prediction challenge now focused on predicting the discriminability of olfactory mixtures. Using publicly available data from 3 different studies (Bushdid et al 2014, Snitz et al 2013, Ravia et al 2020) for more than 700 unique mixtures and almost 600 measurements of mixture pairsdiscriminability, participants will be tasked to predict the discriminability of 46 unpublished mixture pairs. We will here present the details of the datasets and the challenge timeline/scoring approach.