A team of researchers from UC San Diego (CA, USA) have developed a data algorithm for analyzing mass spectral data, which can identify twice as many proteins as traditional techniques.
A study led by scientists at The Scripps Research Institute (TSRI) shows that GC–MS fundamentally alters the sample it analyzes.
A collaborative research effort has yielded a new search method for identifying unknown metabolites.
In this commentary Joe Tweed (Pfizer) discusses how fully leveraging automation in the bioanalytical laboratory requires considerations that should include the supporting logistics and biospecimen management practices routinely used in the laboratory.
Integration of bioanalytical measurements using PK–PD modeling and simulation: implications for antibody–drug conjugate development
In this review the authors discuss the application of PK/PD modeling and simulation for quantitative integration of diverse bioanalytical data available from different stages of ADC development.
This foreword to the Bioanalysis themed issue on stability assessment (Volume 7 Issue 11) outlines the problems associated with stability assessments pertaining to large molecule analytes using LBAs and introduces the articles within the issue.
Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data
This research article outlines the optimization of three aspects of artificial neural networks modeling methodology to improve the computational model implemented in the MolFind/BioSM application.
The University of Florida’s Southeast Center for Integrated Metabolomics held its second SECIM Workshop on May 14, 2015 focusing on metabolomics data processing/analysis and metabolite identification, while providing an overview of metabolomics and an introduction to analytical instrumentation.
This webinar will look at the challenges in peptide quantitation in biopharma, how we overcome these challenges and the benefits of each technology at specific stages of the workflow.