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
In January 2020 the American Diabetes Association released a new set of clinical standards that, for the first time, included the use of an FDA-approved autonomous artificial intelligence (AI) platform for detecting diabetic retinopathy in people with diabetes.
At the end of February physIQ and the US Department of Veteran’s Affairs published data from a clinical trial demonstrating the use of physIQ’s machine-learning algorithm to predict when an individual patient with previously diagnosed heart failure will likely require a readmission to hospital.
Cloud research tools provide a platform for new avenues of scientific research by providing fast access to bare-metal resources.
New processors and GPU technology continue to demand more power than the previous generations, combining this with increasingly dense architectures requires cooling solutions such as liquid or immersion cooling.
Tate Cantrell, CTO, Verne Global, comments on the cost of the AI computing revolution
European researchers are working on a new programming environment for exascale computing that aims to make using these systems more efficient and programmers more productive.
