High Performance Computing for High Content Screening – A Case Study

Using today’s data analysis systems, researchers conducting phenotypic screening campaigns at pharmaceutical companies processing approximately 500,000 compounds estimate image and data analysis time of at least three months.

Furthermore, multiple disparate software systems are used at various stages of the workflow including image analysis, cell level data analysis, well level data analysis, hit stratification, multivariate/machine learning data analysis and visualization, reporting, collaboration, and persistence.

In this webinar, PerkinElmer and AMRI will present a case study wherein high-performance computing (HPC) was leveraged for ultimate performance in image and data analysis of High Content Screening experiments.

Key Learning Objectives
  • Complete Batch re-analysis jobs in days
  • Complete Clustering and other machine learning methods in minutes
  • Balance flexibility, automation, and scalability for large and small organizations and more
Panelists
Seungtaek Lee Seungtaek Lee
Product Manager, PerkinElmer Informatics

Seungtaek Lee is the Product Manager for the TIBCO Spotfire portfolio focused on imaging and drug discovery workflows at PerkinElmer. Seungtaek came into PerkinElmer through the acquisition of Evotec Technologies in 2007 where he held various roles including application support, business development, and team leader for the High Content Screening portfolio. Prior to that, Seungtaek worked for Merck and successful started a centralized High Content Screening lab. Seungtaek holds a master’s degree in Bioengineering from University of Pennsylvania.
James LaRocque James LaRocque
Sr Research Scientist II, Lead Discovery, Albany Molecular Research Inc.

On-Demand Webinar