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Description:
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.
Learning Objectives:
- Recognize the potential of machine learning to facilitate interpretation of genomic data
- Distinguish 6 necessary components to address a problem using machine learning
- Compare the relevance of data models vs. flexibility
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| Speaker Jochen K. Lennerz, MD, PhD |
Moderator Sabah Kadri, PhD |
Level of Instruction: Basic
Date Recorded: September 10, 2019
Planned and coordinated by the Training and Education Committee
Continuing Education Credit Information
CE Credit for this course has expired.Note: Members of AMP can access this webinar at no cost. Join the AMP Family!
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