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

Open Challenges

More information and Submission Details regarding presenting at RSGDREAM 2022 Coming Soon.


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Pre-Register for the scRNA-seq and scATAC-seq Data Analysis DREAM Challenge!

Understanding transcriptional regulation at individual cell resolution is fundamental to understanding complex biological systems such as tissues and organs. Emerging high-throughput sequencing technologies now allow for transcript quantification and chromatin accessibility at the single cell level. Nevertheless, these technologies present unique challenges, due to low amounts of mRNA sequenced per cell (scRNA-seq), and low copy numbers (scATAC-seq), leading to inherent data sparsity. In scRNA-seq, proper signal correction is key to accurate gene expression quantification, which propagates into downstream analyses such as differential gene expression analysis and cell-type specific marker identification. 

The aims of this challenge will be three-fold:
  1. to evaluate computational methods for signal correction and peak identification in scRNA-seq and scATAC-seq, respectively
  2. to assess the impact of these methods on downstream analysis
  3. map scRNA-seq and scATAC-seq data (also known as manifold alignment) for measurements that were collected simultaneously for the same cells.
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Pre-register for Predicting Gene Expression Using Millions of Random Promoter Sequences DREAM Challenge

Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process. To overcome these issues, we have recently created high-throughput measurements of the cis-regulatory activity of millions of randomly generated promoters in the single-cell organism Yeast (de Boer et 2020;).
 
Through this DREAM challenge, participants will be given expression measurements of millions of randomly generated promoter sequences to train machine learning models that predict gene expression from sequences. Computational resources will be provided through the TPU cloud. 

Challenge participants will be asked to develop, train, and validate computational models to predict promoter strength.
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Pre-Register for the Electronic Medical Record NLP DREAM Challenge!

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.

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Pre-Register for the Preterm Birth Prediction: Microbiome DREAM Challenge!

Goal

To develop predictive models for risk of preterm birth in pregnant individuals based on vaginal microbiome data.

There is a controversy whether the vaginal microbiome of pregnant patients differs between patients who deliver term and those who deliver preterm. (1–4) Our challenge seeks to use crowd-sourcing to explore such differences and determine if computational models using vaginal microbiome data will enable improved prediction of preterm birth in order to benefit those who suffer from this condition.

Motivation

Globally, about 11% of infants every year are born preterm, defined as birth prior to 37 weeks of gestation, totaling nearly 15 million births.(5) In addition to the emotional and financial toll on families, preterm births have higher rates of neonatal death, nearly 1 million deaths each year, and long-term health consequences for some children. Infants born preterm are at risk for a variety of adverse outcomes, such as respiratory illnesses, cerebral palsy, infections, and blindness, with infants born very preterm (i.e., before 32 weeks) at increased risk of these conditions.(6)

The ability to accurately predict which women are at a higher risk for preterm birth would help healthcare providers to treat in a timely manner those at higher risk of delivering preterm. Currently available treatments for pregnant women at risk of preterm delivery include corticosteroids for fetal maturation and magnesium sulfate provided prior to 32 weeks to prevent cerebral palsy.(7)

There are several factors known to be associated with PTB, including maternal age, body mass index (BMI), education, smoking, history of PTB, a short cervix, and genetic polymorphisms.(8–11) Nevertheless, there are currently no clinical tools that enable the early and reliable prediction of preterm birth.(12)

There is some indication that the vaginal microbiome plays a significant role in adverse pregnancy outcomes, specifically preterm birth. Previous studies have shown that there are significant differences between the vaginal microbiome of patients who deliver at term and those who deliver prematurely. Vaginal microbiomes with increased diversity as well as communities where Lactobacillus is not dominant have been associated with PTB.(13–15) We hypothesize that this data could be used as a potential avenue for predicting which women are at a higher risk delivering birth.

Patients and healthcare systems alike would benefit from the formation of a precise determination of risk for PTB as outlined in the Challenge.

Pre-Register