Accelerate Biomarker Discovery: Learn How to Mine Data for Actionable Insights

Identifying biomarkers for diagnostic classification and prognostic assessment is increasingly becoming commonplace. This drive to harness an ever-increasing volume and variety of biomarker data has now become critical to help identify patients that could benefit most from emerging therapies. However, leveraging biomarker-driven insights hinges on mining appropriate datasets both for biomarker hypothesis generation and hypothesis testing. This can be especially cumbersome if the necessary bioinformatics skills to extract the best information out of datasets is missing. Simplifying routine data searching, access and analysis also helps your bioinformaticians to focus on answering more complex data queries and analysis.

In this webinar, the presenters will focus on how scientists with minimal bioinformatics skills are able to easily mine large datasets to not only extract the most relevant biomarker information from published abstracts but also to test that hypothesis on their biomarker datasets to assess the impact of biomarker(s) on patient populations.

Join this webinar and learn how to:

• Use Causaly’s powerful AI/machine reading-driven technology to extract contextual biomarker information from unstructured sources such as 30million+ PubMed abstracts
• PerkinElmer Signals™ Translational’s data management and analytics capabilities to search, access and analyze structured datasets totest biomarker hypotheses and identify patient cohorts.
• This session is ideally suited to scientists, senior stakeholders in translational research, precision medicine, and biomarker discovery.

Speakers

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

Dr. 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), Sharma is focussed on driving commercial and product strategy around data access, integration and advanced analytics of translational research data. Her previous roles have included senior data analytics application specialist at PKI as well as Genomics-focused application-based roles at UCL Genomics and Integromics.

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Artur Saudabayev, CTO and Co-founder, Causaly

Artur Saudabayev is a co-founder and the CTO of Causaly. Artur is a computer scientist with almost 7 years experience in academia where he worked on machine learning research for natural language and image processing in robotics. During his MSc studies at the University of Edinburgh, Saudabayev focused on bioinformatics and systems biological approaches for novel target prediction.

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Dr. Danny Martinez-Herrera, Field Application Scientist, PerkinElmer Informatics

Dr. Danny Martínez-Herrera is currently a field application scientist at Perkin Elmer Informatics (PKI). He is mainly focused on helping other scientists test their biomarker hypotheses and stratify patients more effectively, with PKI´s solutions for Translational Research. With a solid background in biochemistry and NGS data analysis, he earned a PhD in Molecular Biology and Bioinformatics from the Autonomous University of Madrid in the year 2017.

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