Robert Roe looks at the use of precision medicine and its potential impact on laboratory informatics software
Data types used are advancing from the simple text formats of old, writes Paul Denny-Gouldson
The failure of a candidate drug can cost millions – so many chemists are turning to software that provides modelling capabilities and multi-parameter optimisation
Robert Roe interviews laboratory informatics software providers who discuss potentially disruptive technologies and their impact on the laboratory informatics market
Darren Barrington-Light, senior manager product marketing for Thermo Fisher Scientific, explains the importance of integrating LIMS into the pharmaceutical data chain
Welcome to our Laboratory Informatics Guide 2018.
Over the few years that Europa Science has been producing the Laboratory Informatics Guide, one constant has been the fact that the informatics industry does not stand still for long.
In this guide, we look at the effect that artificial intelligence (AI) technology is having on healthcare research, how 2017 was a year of change for laboratory informatics, what the future looks like, and much more.
This year the Paperless Lab Academy (#PLA2018) conference will move to a new location overlooking Lake Maggiore on the southern side of the Alps, near Milan, Italy. The central theme will focus on how to ‘Empower your eData Life Cycle.’
Sophia Ktori reveals the informatics company’s history – and plans for the future
Elsevier’s Jabe Wilson predicts radical changes in the ways AI will be used in scholarly communications
David Wang gives his view on how modern laboratories can leverage data to provide maximum value
Building a Smart Laboratory 2018 highlights the importance of adopting smart laboratory technology, as well as pointing out the challenges and pitfalls of the process
Informatics experts share their experiences on the implementing new technologies and manging change in the modern laboratory
This chapter will consider the different classes of instruments and computerised instrument systems to be found in laboratories and the role they play in computerised experiments and sample processing – and the steady progress towards all-electronic laboratories.
This chapter considers how the smart laboratory contributes to the requirements of a knowledge eco-system, and the practical consequences of joined-up science. Knowledge management describes the processes that bring people and information together to address the acquisition, processing, storage, use, and re-use of knowledge to develop understanding and to create value
This chapter takes the theme of knowledge management beyond document handling into the analysis and mining of data. Technology by itself is not enough – laboratory staff need to understand the output from the data analysis tools – and so data analytics must be considered holistically, starting with the design of the experiment