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Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex output and the decision to include a variant in the final report remains challenging. Machine learning is one approach to mine big data and derive models for decision-making. Given that bioinformatics pipelines generate mostly structured, discrete data, the setting is ideal to assess a machine learning decision support system. A decision support tool for variant reporting is a relevant approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities. How can this be accomplished? What are other use cases? What are the concrete steps for implementation? In this upcoming webcast, Dr. Joe Lennerz from the Center for Integrated Diagnostics, Massachusetts General Hospital/Harvard Medical School will address these questions.
Speaker: Jochen K. Lennerz, MD, PhD
Moderator: Sabah Kadri, PhD
Duration: 1 hr
Level of Instruction: Intermediate
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* You may not submit SAMs and CME/CMLE credit for the same content.
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September 10, 2019
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AMP Technologist Member: $0.00
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Non-member Price: $195.00