NEWS

Artificial intelligence could finally help researchers tell a molecule’s odor from its structure.  Light and sound are predictable. Smells are not.  (Read more)

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.)

The Ninth Annual RECOMB/ISCB Conference on Regulatory and Systems Genomics, with DREAM Challenges and Cytoscape Workshop is now accepting abstracts for oral presentations and posters. Topics of interest range from Network visualization and analysis to Translational systems biology.  (Learn more.)

The Cancer Moonshot is hosting a summit at Howard University, in Washington, D.C.  In conjunction with the Summit, the Vice President is announcing a set of new public and private sector actions to drive progress toward ending cancer as we know it.  Federal agencies have come together as part of the Cancer Moonshot Task Force and are announcing today additional investments, improved policies, and new private sector partnerships focused on catalyzing new scientific breakthroughs, unleashing the power of data, accelerating bringing new therapies to patients, strengthening prevention and diagnosis, and improving patient access and care.  Among the initiatives announced today was the official launch of the first of the Coding4Cancer (C4C) Challenges: the Digital Mammography DREAM Challenge. (Read more.)

DREAM recently both partnered with the Boston Computational Biology and Bioinformatics network and joined the Systems Biology of Human Disease conference to announce winners and present the initial results from the AstraZeneca-Sanger Drug Combination Prediction Challenge.   Jonathan Dry and Julio Saez-Rodriguez reported to an audience of over 80 computational biologists how this Challenge became the highest participated of any DREAM challenge to date, with participation from across the globe including individuals qualified in a wide range of disciplines.  They highlighted the winning method from Yuanfang Guan (Department of Computational Medicine & Bioinformatics, University of Michigan), and revealed fascinating early insight to the patterns of data and method usage most influential in successful predictions.  DREAM were also delighted to welcome Peter Sorger (Professor of Systems Pharmacology, Harvard Medical School) to describe his own take on crowdsourcing and drug combinations at the BCBB event.

A commentary in the most recent issue of the journal Nature discusses how DREAM Challenges and other open competitions bring new minds, skills and collaborations to problems in biomedical research. (Read More)

BCBB is excited to partner with DREAM as they present the results of their Drug Combination Prediction Challenge at a special BCBB Speaker event . ”DREAMing of Better Drug Combinations” (Read more).

An international consortium of groups from Canada, the United States and the United Kingdom have come together to create an innovative, cloud-based, public challenge running on the Google Cloud Platform to optimize the discovery of genetically distinct groups of cells within cancers that could respond differently to treatment and have different risk of spreading. (Read more)