DREAM Challenges
DREAM Challenges engage the scientific community to collaboratively solve fundamental biomedical questions. Sage Bionetworks provides the expertise and infrastructure to run DREAM Challenges on their Synapse platform. Together, we share a vision where individuals and groups collaborate openly so that the “wisdom of the crowd” translates into a significant impact on science and human health.
The F1000Research DREAM Challenges gateway provided a venue for groups involved in the challenges to publish peer-reviewed articles, mainly method articles and software tool articles. This allowed researchers additional credit and visibility for their work as a challenge participant.
This challenge will attempt to improve the prediction of survival and toxicity of docetaxel treatment in patients with metastatic castration-resistant prostate cancer (mCRPC). The primary benefit of this Challenge will be to establish new quantitative benchmarks for prognostic modeling in mCRPC, with a potential impact for clinical decision making and ultimately understanding the mechanism of disease progression. Participating teams will be asked to submit predictive models based on clinical variables from the comparator arms of four phase III clinical trials with over 2,000 mCRPC patients treated with first-line docetaxel. The comparator arm of a clinical trial represents the patients that receive a treatment that is considered to be effective. This arm of the clinical trial is used to evaluate the effectiveness of the new therapy being tested.
The level by which genes are transcribed is determined in large part by the DNA sequence upstream to the gene, known as the promoter region. Although widely studied, we are still far from a quantitative and predictive understanding of how transcriptional regulation is encoded in gene promoters. One obstacle in the field is obtaining accurate measurements of transcription derived by different promoters. To address this, an experimental system was designed to measure the transcription derived by different promoters, all of which are inserted into the same genomic location upstream to a reporter gene – a yellow florescence protein gene (YFP). The challenge consists of the prediction of the promoter activity given a promoter sequence and a specific experimental condition. To study a set of promoters that share many elements of the regulatory program, and thus are suitable for computational learning, the data pertains to promoters of most of the ribosomal protein genes (RP) of yeast (S.cerevisiae), in rich medium condition (SCD).
In this Challenge on Single-Cell Transcriptomics, participants will reconstruct the location of single cells in the Drosophila embryo using single-cell transcriptomic data.
The Disease Module Identification DREAM Challenge is an open community effort to systematically assess module identification methods on a panel of state-of-the-art genomic networks and leverage the “wisdom of crowds” to discover novel modules and pathways underlying complex diseases.
The Malaria DREAM Challenge is open to anyone interested in contributing to the development of computational models that address important problems in advancing the fight against malaria. The overall goal of the first Malaria DREAM Challenge is to predict Artemisinin (Art) drug resistance level of a test set of malaria parasites using their in vitro transcription data and a training set consisting of published in vivo and unpublished in vitro transcriptomes.