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.
Artificial intelligence could finally help researchers tell a molecule’s odor from its structure. Light and sound are predictable. Smells are not. (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.)