Machines don’t have noses – but they can now attempt to identify scents thanks to a nifty new algorithm.
Author Archive | Dreameditor
Schlessinger A, et. al.; Multi-targeting Drug Community Challenge.; Cell Chem Biol. 2017 Dec 21;24(12):1434-1435. doi: 10.1016/j.chembiol.2017.12.006.
Azencott CA, et. al.; The inconvenience of data of convenience: computational research beyond post-mortem analyses.; Nat Methods. 2017 Sep 29;14(10):937-938. doi: 10.1038/nmeth.4457.
Fatemeh Seyednasrollah, et. al. A DREAM Challenge to Build Prediction Models for Short-Term Discontinuation of Docetaxel in Metastatic Castration-Resistant Prostate Cancer JCO Clinical Cancer Informatics – published online August 4, 2017, DOI: 10.1200/CCI.17.00018
Trister AD, Buist DSM, Lee CI. Will Machine Learning Tip the Balance in Breast Cancer Screening?. JAMA Oncol. Published online May 04, 2017. doi:10.1001/jamaoncol.2017.0473
Keller A, Gerkin R, et. al. Predicting human olfactory perception from chemical features of odor molecules; Science; 24 Feb 2017; Vol 355; Issue 6327; 820-826; DOI: 10.1126/science.aai2014
Although the Roman philosopher Lucretius was right when he wrote that odors were caused by a flow of atoms emitted by objects, smell may still be the least understood of our five senses. While we use it every day, science has not fully understood how molecules produce an odor, or how to determine what they smell like without sniffing them. We have known for decades that for sight, seeing requires wavelengths of light, and for sound, hearing is done through tonal frequencies, but the olfactory-code remained unbroken – until now. (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.)
Nov 6-9, 2016, Phoenix, AZ
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.