Multiple Myeloma DREAM Challenge
Opening January 2017
The Multiple Myeloma DREAM Challenge provides an opportunity to combine integration of large scale molecular and clinical data and state of the art analytical approaches to facilitate risk stratification in over 25,000 patients in the US alone. Additionally, it provides the ability to benchmark novel methods with the greatest potential to yield patient care benefits in the future.
ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge
July 7, 2016 -January 17, 2017 (closed)
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.
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.
Algorithms can predict a molecule’s odour on the basis of its chemical structure.
Pablo Meyer at IBM’s Computational Biology Center in Yorktown Heights, New York, and his colleagues, asked 49 people to smell hundreds of molecules (pictured) and rate them on intensity, pleasantness and 19 other descriptors, such as ‘fruit’, ‘musky’ and ‘bakery’. (Read More)
Predicting color is easy: Shine a light with a wavelength of 510 nanometers, and most people will say it looks green. Yet figuring out exactly how a particular molecule will smell is much tougher. Now, 22 teams of computer scientists have unveiled a set of algorithms able to predict the odor of different molecules based on their chemical structure. It remains to be seen how broadly useful such programs will be, but one hope is that such algorithms may help fragrancemakers and food producers design new odorants with precisely tailored scents. (Read more)
A team of researchers and volunteers from across the globe have trained computers to predict the way a molecule will smell based on its structure. The feat may help scientists unravel the still-mysterious relationship between molecular structure and odor perception (read more)