Artificial Intelligence and Machine Learning – Multimedia

Deciding to implement ontologies into your data management practices can be daunting and a difficult sell to business stakeholders. This article outlines the challenges and offers 10 guidelines for kickstarting your ontologies journey.

Speaking at the Advances in Proteomics and Metabolomics online symposium 2021, Matthias Mann delivered his talk on ultra-high sensitivity proteomics for digital cancer pathology.

In this Teach Me In 10 episode we are joined by Dr. Nicky Keay, a leading expert in endocrinology and honorary clinical lecturer in medicine at University College London, honorary fellow in the Department of Sport and Exercise Sciences at Durham University and former research fellow at St. Thomas’ Hospital.

In conventional widefield imaging, a common issue is out-of-focus blur, especially when imaging thick sections or whole-mounted samples like the retina.

If your gas chromatograph is only running part time, you’re missing the benefits of a system that delivers measurably more production time. That’s the advantage of the time- and space-saving Thermo ScientificTM TRACETM 1600 Series Gas Chromatograph (GC).

A neural network could help historians figure out when and where a piece of writing comes from - as well as fill in missing words and characters.

This listicle will explore five key risk factors pharmaceutical facilities need to understand, which they can then ultimately renovate to improve output.

In the first episode of the series, Greg Clark at Thermo Fisher helps us to understand the challenges faced by scientists in managing and interpreting imaging data today and just how important AI and Machine learning are to the future of imaging.

The specificity of fluorescence microscopy allows researchers to accurately observe and analyze biological processes and structures quickly and easily, even when using thick or large samples. However, out-of-focus fluorescence increases background, reduces contrast and makes accurate image segmentation more challenging.

Researchers from the Mark Foundation Institute for Integrated Cancer Medicine at the Cancer Research UK Cambridge Centre are bringing together complex data with machine learning techniques to help clinicians make better decisions for cancer patients.