NCI-CPTAC DREAM Proteogenomics Challenge
Opening Spring 2017
This challenge will use public and novel proteogenomic data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) to benchmark an understanding of the interfaces between different layers of information in a population of cancer cells.
Multiple Myeloma DREAM Challenge
Opening Spring 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.
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.
Multi-institutional randomized clinical trials have been a feature of oncology research in the United States since the 1950s. Since that time, cancer-treatment trials have been continuously funded by the National Cancer Institute (NCI) through a program that has evolved to become the National Clinical Trials Network (NCTN). Currently, approximately 19,000 patients with cancer participate in NCTN clinical trials each year. Approximately 70,000 additional patients with cancer are enrolled each year in treatment trials sponsored by the pharmaceutical industry (Read more.)
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)