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)
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)
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)
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)
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)
Whenever you say a color name, you’re referring to specific properties of light waves. Sounds work the same way, but with properties of compression waves. But what about smell? With all of the different scented chemicals out there and their complex interactions, it’s been impossible to create a simple scale to describe the odors or noses detect. (read more)
Machines don’t have noses – but they can now attempt to identify scents thanks to a nifty new algorithm.
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)