DREAM News
October 2, 2014

NEWS

January 17, 2018

Winners in the DREAM Parkinson’s Disease Digital Biomarker Challenge Announced

Sage Bionetworks in Collaboration with The Michael J. Fox Foundation Announce Winners in the DREAM Parkinson’s Disease Digital Biomarker Challenge. Sage Bionetworks announced today the results of the Parkinson’s Disease Digital Biomarker (PDDM) DREAM challenge, an open crowd-sourced research project designed to benchmark the use of remote sensors to diagnose and track Parkinson’s disease (PD). The winners of this Challenge developed methods that are 38% better than previous models at detecting Parkinson’s disease from a simple walk and balance test (read more)

November 13, 2017

New DREAM Video

Calling all DREAMers…  The DREAM team is developing a video project about DREAM and our community, and needs your help.  We need Selfies of you holding signs that say either  “DREAM Challenges”  or “DREAM” or “Wisdom of the Crowd” .  Take a sheet of white unlined copier paper and write as big as you can with a marker (so it is legible).  You can take one selfie with one message or three.   Then we will include them in the video of our community of DREAMers adding to the Wisdom of the Crowd.  (12/6/17 – Thanks for your help.. this is done.. Please see video on the DREAM home page or on Youtube – https://youtu.be/PrAA-DnTQ7w)

July 24, 2017

DREAM Challenge Community competition launches to learn how to use smartphone and wearable sensors to monitor health

Recent advances in mobile health have demonstrated great potential to leverage sensor-based technologies for remote monitoring of health and disease – particularly for diseases affecting motor function such as Parkinson’s. While there are many projects that have successfully collected sensor data from people in the real-world setting, researchers still have a poor understanding of what the data can tell us about health. (Read more.)

June 2, 2017

IBM and Sage Bionetworks announce winners of first phase of DREAM Digital Mammography Challenge

IBM and Sage Bionetworks announced today the winners of the first phase of its DREAM Digital Mammography (DM) Challenge have developed algorithms that had 5% fewer false-positive errors in breast cancer screenings than recently published state of the art computerized methods1. This 5 percent improvement could potentially lead to less anxiety and unnecessary procedures for an estimated two million women per year in the United States and could help reduce costs associated with follow-up exams and biopsies. (Read more)

June 2, 2017

Can Machine Learning Help Improve Accuracy in Breast Cancer Screening?

Breast Cancer is the most common cancer in women. It is estimated that one out of eight women will be diagnosed with breast cancer in their lifetime. The good news is that 99 percent of women whose breast cancer was detected early (stage 1 or 0) survive beyond five years after diagnosis, leading countries around the world to implement breast cancer screening programs for early detection. (Read more)

May 5, 2017

Will Machine Learning….

New commentary from the organizers of the DREAM Digital Mammography Challenge in JAMA Oncology asks “Will Machine Learning Tip the Balance in Breast Cancer Screening?” The article discusses the ongoing challenge and how this DREAM Challenge is working to answer that question. (Read more)

March 31, 2017

Advantages of a Truly Open-Access Data-Sharing Model

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.)

March 17, 2017

Predicting smell from structure

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)

March 8, 2017

Artificial intelligence grows a nose

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)

March 8, 2017

Computers predict molecules’ scent from their structures

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)