PerkinElmer Signals Translational: All is FAIR in Biomarker Discovery and Patient Stratification

Clinical Trials, Biomarkers,
  • Tuesday, March 24, 2020

A successful biomarker-driven clinical trial depends on optimal patient stratification. This is increasingly being restricted by the growing complexity, diversity and sheer size of translational medicine data. Empowering translational scientists to explore and analyze translational and clinical datasets for successful biomarker discovery is fraught with challenges. Furthermore, organizations need to establish best practices that adhere to FAIR guidelines for data management and data stewardship.

In this webinar, viewers will learn how signals translational can help with biomarker discovery and patient stratification, and how Bristol-Myers Squibb (BMS) have integrated PerkinElmer Signals Translational in their ecosystem whilst keeping the focus on FAIR guiding principles:

  • Findable: A collaborative framework for effective sharing of data, analytics and insights
  • Accessible: Universal data access for effective cross-study analysis of translational research and clinical data
  • Interoperable: Flexible yet robust information model that ensures harmonization of datasets for cross-study analysis
  • Repeatable: Establish best practices by creating and sharing repeatable analytics protocols (Powered by TIBCO Spotfire) to test and adapt hypotheses across the organization


Dr. Simone Sharma, Strategic Lead- Translational Analytics, PerkinElmer Informatics

Simone Sharma has a background in molecular genomics and received her PhD from University College London in 2010. In her current role as Strategic Lead for Translational Analytics at PerkinElmer Informatics (PKI), Simone is focussed on driving commercial and product strategy around data access, integration and advanced analytics of translational research data. Her previous roles have included Snr. Data Analytics Application Specialist at PKI as well as Genomics focussed application based roles at UCL Genomics and Integromics.

Message Presenter

Dr. Ying (Sherry) Li, Associate Director, Translational Medicine IT Business Partner, Bristol-Myers Squibb

Dr. Li has extensive experience in the pharmaceutical discovery and clinical informatics field.  She received her Ph.D. in molecular biology from Yale University. After developing interests in data analysis, she pursued a path towards bioinformatics/IT business analyst early in her career.  Dr. Li is passionate about using big-data to impact decision making. Due to her long tenure in the field and her firsthand knowledge in using informatics methods/tools to facilitate pharmaceutical research and clinical study, she has gained broad insight in assessing informatics solutions and delivering realistic IT benefits for pharmaceutical scientists. Dr. Li has been engaged in the implementation of Signals Translational at BMS for their clinical biomarker program.

Message Presenter

Who Should Attend?

The webinar will be suitable for Translational/ Biomarker Scientists and IT personnel as well as individuals with the following job titles:

  • Precision Medicine Group Leaders/Scientists
  • Translational Research Group Leaders/Scientists
  • Biomarker Programme Leaders/Scientist
  • Translational R&D IT Leaders
  • Bioinformatics Groups/Scientists

What You Will Learn

Learn how signals translational adheres to FAIR guiding principles to help you establish best practices to:

  • Find and access relevant datasets
  • Prepare datasets for cross-study analysis
  • Perform self-service and repeatable cross-study analytics

Learn how Bristol Myers Squib (BMS) are leveraging signals translational in their own ecosystem to provide data access to their stakeholders in a FAIR, collaborative and secure environment to ensure effective biomarker discovery.

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PerkinElmer’s advanced analytics and services solutions for Clinical Development help the world’s leading biopharmaceutical, medical device and diagnostics manufacturers discover new therapeutics faster by streamlining clinical operations, transforming risk into safety and enabling actionable decisions that can lead to better health outcomes.

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