Different data, different analytical tools, different users. How can scientific computing cope with diversity in the life sciences? Tom Wilkie discovers that data pipelining and workflow technology offer new approaches
Felix Grant explores the applications for - and reasoning behind - pattern recognition
Commercial laboratories increasingly turn to less-skilled staff for their routine analyses, but also want to use and share their results in far more ways than in the past. The key to achieving these different goals lies in the software, writes Siân Harris
Andre Geim, director, Centre for Mesoscience and Nanotechnology, University of Manchester
Robert Roe interviews MathWorks Loren Dean, on the use of AI in modelling and simulation
Every device needs to work seamlessly in its operating environment. But when that environment is a human body, things get complicated.
Christian Marcazzo, general manager at IDBS, highlights trends in life sciences research and development.
Data is a company’s biggest asset, yet for any organisation, keeping a handle on the potentially vast volumes and diversity of data that are generated can represent a considerable issue.
Rob Lalonde, Univa’s cloud VP general manager, considers the unique challenges posed by HPC
Deep learning has seen a huge rise in popularity over the last five years in both enterprise and scientific applications.
HPC users are increasingly turning to cloud technologies due to their flexibility and scalability, allowing them to quickly change the size of their workloads, adopt new technologies in a small testing environment and to help to increase the agility of a company working across multiple sites, or
