Drug development is facing change – both from technological pressures, such as the use of AI and machine learning, plus new regulations which are driving sweeping changes to the way electronic records are created and stored for clinical trials.
Features
Intel’s Cedric Andreolli, Jim Cownie and Kate Antakova explain how the Roofline model can be used to improve HPC code
Robert Roe reports on the Gordon Bell Prize finalists for 2018, as the list is dominated by AI and deep leaning applications running on the Summit supercomputer
Robert Roe takes a look at supercomputing development that is paving the way for the first generation of exascale systems
Mark Newton, a consultant from Heartland QA, gives his take on the scale of research misconduct taking place in laboratories
Dr Alexander Jarasch and Professor Martin Hrabe de Angelis explain that novel research methods produce tremendous amounts of data that cannot be analysed with classic analysis tools – so scientists need to look for new approaches, such as graph technology
Darren Barrington-Light, senior manager product marketing for Thermo Fisher Scientific, explains the importance of integrating a LIMS software package into the pharmaceutical data chain
Robert Roe talks to laboratory software providers about their plans for the future of the laboratory
Robert Roe speaks with laboratory informatics software providers about the future of their software and the introduction of new technologies, such as AI and deep learning
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Robert Roe speaks with laboratory informatics software providers about the future of their software and the introduction of new technologies, such as AI and deep learning
Scientists are now beginning to use new technologies such as the internet of things (IoT), artificial intelligence (AI) and machine learning (ML) in their daily workflows.
In today’s world, where drug development integrates science and technology, consumer safety is paramount in the pharmaceutical industry.