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.
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.
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.
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.
Who Should Attend?
- VP/ Director – Head of Translational/ Precision Medicine
- Translational ResearchScientists/ Biomarker Scientists
- Biomarker DiscoveryGroups
- Region: EMEA/ USA
What You Will Learn
Participants will learn how to effectively mine textual and biomarker datasets to help with biomarker discovery:
- Extracting the most relevant contextual information to inform your biomarker hypothesis
- Mining biomarker datasets to test your biomarker hypothesis and identify patient populations
- Performing self-service and repeatable analysis for biomarker discovery
PerkinElmer’s advanced analytics and services solutions for Translational and Clinical Analytics helps the world’s leading biopharmaceuticals to discover new therapeutics faster by streamlining data access, integration and analytics to enable actionable decisions that can lead to better health outcomes. Using state of the art technologies to enhance biomarker discovery, patient selection and clinical data review, PerkinElmer focuses on delivering intuitive, user-centric solutions.