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600 measurements of mixture pairsdiscriminability, participants will be tasked to predict the discriminability of 46 unpublished mixture pairs.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.
Calling all champions of discovery and health enthusiasts! Join us in a thrilling challenge to unravel the complexities of hypercholesterolemia. Your participation is vital as we harness the power of multi-dimensional data to decode the secrets hidden within our genes, the environment, diet, and lifestyle. Together, let’s embark on this exhilarating journey of exploration and innovation, as we strive to uncover the collective impact of these factors on human health. Are you ready to accept the challenge and make a difference in the fight against hypercholesterolemia? Join us now and be part of the solution!
A critical bottleneck in translational and clinical research is access to large volumes of high-quality clinical data. While structured data exist in medical EHR systems, a large portion of patient information including patient status, treatments, and outcomes is contained in unstructured text fields. Research in Natural Language Processing (NLP) aims to unlock this hidden and often inaccessible information. However, numerous challenges exist in developing and evaluating NLP methods, much of it centered on having “gold-standard” metrics for evaluation, and access to data that may contain personal health information (PHI).
This DREAM Challenge will focus on the development and evaluation of of NLP algorithms that can improve clinical trial matching and recruitment. !
