Latest Articles

Professor Michael Snyder performed the first longitudinal detailed integrative personal omics profile and used this to assess disease risk and monitor disease states. Technology Networks invited Snyder to an Ask Me Anything session to answer your questions about the future of digital health.

Bayesian statistics has emerged as a powerful methodology for making decisions from data in the applied sciences. Bayesian brings a new way of thinking to statistics, in how it deals with probability, uncertainty and drawing inferences from an analysis.

Kieran O'Regan, COO and co-founder of About:Energy, works to bridge the gap between universities and industry and accelerate the development of new battery technologies. Technology Networks invited Kieran O'Regan to an Ask Me Anything session to answer your questions about this incredible technology.

Current automated insulin delivery systems for Type 1 diabetics struggle to regulate blood glucose effectively during exercise, but integrating data from wearable fitness sensors and AI could change that, a recent study suggests.

Digitalization underpins the biopharma industry’s endeavors to keep up with innovation and get life-changing therapeutics into the hands of patients as quickly as possible. This article explores how a digital backbone can help to realize the full potential of data and generate meaningful insights.

Biomarkers can potentially aid patient management across the stages of their disease course. This article will review advances in computationally-driven biomarker discovery, including from metabolomics datasets, for complex, multifactorial diseases.

In this article, we explore ways analytical scientists can improve their data reporting and sharing practices, leading to improvements in data integrity and regulatory compliance.

By decoding the biological lexicon and integrating diverse forms of data, AI offers new possibilities for modeling biology and designing therapies. In this article, we explore the potential of recent advancements in AI to revolutionize drug R&D.

In this article, we discuss how the most advanced laboratory informatics software can enhance the scientific experience across both physical lab work and data management and analysis, enabling analysts to focus on what matters – science.

This article explores why the traditional process of sorting through RWD is a challenge, what value is lost through that process, and the underlying technology needed to be able to filter and sort data in a compliant manner.