Upon implementing electronic medical record-based interventions, Boston Medical Center reduced unnecessary diagnostic testing and increased the use of postoperative order sets, two markers of providing high-value medical care.

MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery.

Based on an analysis of data leakages and opportunities to prevent the potential misuse of genetic information, researchers have developed a new file format for functional genomics data that enables data sharing while protecting the personal information of research participants.

The lipidome refers to the totality of lipids in cells. In this article, PREMIER Biosoft's Sanjib Meitei explains how his team developed a new technique that can quantitate and separate lipidome sammples in just 15 minutes.

Being able to make conclusions about data trends is one of the most important steps in both business and science. Through the magic of least sums regression, and with a few simple equations, we can calculate a predictive model that can let us estimate our data and give us much more power over it. If you want a simple explanation of how to calculate and draw a line of best fit through your data, read on!

Integrating new technologies into laboratory workflows can be a complex transition. This article outlines why, when it comes to diagnosing infections, it's an investment worth making.

Mass spectrometry imaging (MSI) is a powerful tool increasingly being used in many fields including cancer research, biomarker discovery and drug development. This article explains why MSI is the technique of choice in many labs, discusses its use in research, and covers how some inherent challenges are being addressed.

New technologies are driving laboratory automation to improve efficiency, shorten time to FDA approval and reduce the number of technicians needed in the lab – driving overall development costs down.

Artificial intelligence (AI) has begun to transform the world of healthcare and researchers across the globe are now dedicated to discovering its potential. This technology has particularly struck a chord with cancer research surrounding early detection, a long-standing concern for the cancer community.