Verhaak Receives Agilent Early Career Professor Award
Agilent Technologies Inc. has named Roeland G.W. Verhaak, of the Jackson Laboratory (JAX), the winner of the company’s Early Career Professor Award. Verhaak was selected for his contributions to the implementation of transcriptomics, genomics, and big-data analysis to the classification and diagnosis of various cancer types.
Agilent Technologies Inc. has named Roeland G.W. Verhaak, of the Jackson Laboratory (JAX), the winner of the company’s Early Career Professor Award. Verhaak was selected for his contributions to the implementation of transcriptomics, genomics, and big-data analysis to the classification and diagnosis of various cancer types.
“Agilent is pleased to recognize Dr. Verhaak with this award and to support his important research at JAX,” said Jack Wenstrand, Agilent’s director of university relations and external research.
“Dr. Verhaak is recognized internationally for the development and implementation of workflows for the analysis of big-data from transcriptomics to next generation sequencing approaches. His data-driven, unbiased analyses of cancer genomics and profound understanding of cancer biology are improving our ability to identify clinically relevant subtypes of cancer,” continued Wenstrand.
Established in 2008, the Agilent Early Career Professor Award seeks to recognize the achievements of academic researchers in the early stages of their careers and to establish strong collaborative relationships.
“We’re so pleased that Dr. Verhaak has received this prestigious award in recognition of his outstanding work in advancing human health,” said Charles Lee, Ph.D., FACMG, scientific director and professor at JAX Genomic Medicine.
For more information, please visit: www.agilent.com/about/newsroom/presrel/2016/18oct-ca16031.html
AI and GenAI Applications to Help Optimize Purification and Yield of Antibodies From Plasma
October 31st 2024Deriving antibodies from plasma products involves several steps, typically starting from the collection of plasma and ending with the purification of the desired antibodies. These are: plasma collection; plasma pooling; fractionation; antibody purification; concentration and formulation; quality control; and packaging and storage. This process results in a purified antibody product that can be used for therapeutic purposes, diagnostic tests, or research. Each step is critical to ensure the safety, efficacy, and quality of the final product. Applications of AI/GenAI in many of these steps can significantly help in the optimization of purification and yield of the desired antibodies. Some specific use-cases are: selecting and optimizing plasma units for optimized plasma pooling; GenAI solution for enterprise search on internal knowledge portal; analysing and optimizing production batch profitability, inventory, yields; monitoring production batch key performance indicators for outlier identification; monitoring production equipment to predict maintenance events; and reducing quality control laboratory testing turnaround time.
