Advances in Protein Engineering
In this article, we look at two of the hottest areas in protein engineering for therapeutic applications and how machine learning and artificial intelligence is set to transform the field.
AI Beats Standard Risk Model for Predicting Future Breast Cancer
AI algorithms identify both missed breast cancers and tissue features may predict future cancers.
AI Model Predicts Human Emotions
Using insights into how people intuit others’ emotions, researchers have designed a model that approximates this aspect of human social intelligence.
Technique Harnesses Light To Advance Brain-Like Computing
Engineers are exploring how optical resistors with memory may be key to developing neuromorphic computing.
The Glue of Genomics: Will Science’s Unsung Data Heroes Abandon Academia?
This article will review the latest advances in next-generation sequencing technologies, data analysis tools and their various applications with key research examples.
Multidimensional Mass Spectrometry and Machine Learning: A Recipe for Richer Metabolomics
A new metabolomics workflow has been developed that combines state-of-the-art analytical instrumentation, which generates information-rich data, with a novel machine learning-based algorithm tailored to process it.
Improving Image Integrity in Scientific Papers
This article sheds light on the issue of image integrity in academic publishing and gives advice on how to reduce the risk.
How To Guide
How To Reduce Data Integrity Risk
The aim of this guide is to provide practical advice on how to reduce data integrity risk and ensure GxP compliance. Learn from those in the industry who have failed to comply with data integrity regulations and who have been on the receiving end of an FDA inspection.
How To Guide
Revolutionary Binding Kinetics Analysis
Conventional bioassays often require labeled detection reagents to provide a measurable readout. However, modifications to molecular structure and/or function can skew results, and non-specific binding of the labels themselves can lead to an unwanted background signal.
Ten Guidelines for Adopting Ontologies To Create FAIRer Scientific Data
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.
Standardizing Analytical Data With AnIML
This listicle presents AnIML — an open XML-based, vendor-neutral and human-readable file format that can be used to store data from different analytical instruments. This facilitates the achievement of FAIR (i.e., findable, accessible, interoperable and reusable) data which is a prerequisite for proper data management, data stewardship and a successful digital transformation strategy.