ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge
July 7, 2016 -January 11, 2017 (open)
The goal of this Challenge is to identify the best method for predicting in vivo transcription factor binding sites across cell types and tissues by integrating DNA sequence, RNA expression and chromatin accessibility data.
The Digital Mammography DREAM Challenge.
June 29, 2016- Feb. 20, 2017 (open)
With generous support from the Laura and John Arnold Foundation this $1.2 million Challenge, one of two large prize Coding4Cancer Challenges, seeks to improve the accuracy of breast cancer detection and reduce the current rate of patient callbacks.
Disease Module Identification DREAM Challenge
June 24- Oct. 3, 2016 (Now Closed)
The goal of this Challenge is to (1) systematically assess module identification methods on the latest molecular networks and (2) discover novel network modules/pathways underlying complex diseases.
ICGC-TCGA DREAM Somatic Mutation Calling RNA Challenge (SMC-RNA)
Summer 2016 (open)
Leaders from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) have come together to develop a Challenge to assess the accuracy of methods to work with cancer RNA Sequencing data.
DREAM Idea Challenge
June 15 — Late Fall 2016 (open)
The goal of the challenge is to solicit mathematical models that are ripe to make major discoveries given the data, based on solid analysis.
As part of Vice President Joe Biden’s $1 billion Cancer Moonshot initiative, hundreds of scientists and coders are attempting to improve the ability of mammograms to detect breast cancer. (Read more.)
Participants in the Digital Mammography DREAM Challenge are trying to do their part to contribute to the nationwide goal of completing 10 years of cancer research in half the time. It’s funded under the Cancer Moonshot’s Coding4Cancer initiative —pitting coding teams against each other in a friendly fight to see who can come up with the best way to improve mammogram readings. (Read more)
Scientists from around the world have announced a new challenge to find the best algorithms for detecting all of the abnormal RNA molecules in a cancer cell. This is a community effort, inviting all scientists and enthusiasts to participate in a collaborative crowd-sourced benchmarking effort. Based on the success of other recent SMC-Het challenges, the new SMC-RNA challenge will use a cloud model in which contestants submit their algorithms, not their results, to the evaluation. It will be the first challenge to make use of the new NCI Cloud Pilots. (Read more.)