Please note that this content was created in 2018. In the time since this material was posted, there may have been additional developments, advancements, and/or more current publications in this field.
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This is a recording of a session from the AMP 2018 Annual Meeting & Expo. Purchase the entire AMP Annual Meeting & Expo 2018 Recordings for a significant discount!
From sequencing to phenotypic information, the amount of data within the molecular diagnostics laboratory is increasing at an ever-rapid pace with the emergence of new technologies and structured data sources. While dealing with this influx of information requires new analyses, it also presents an opportunity to learn from the data to provide novel insights that would otherwise be difficult to identify. This session will present an overview of these analysis methods, including artificial intelligence and machine learning approaches, and their current and future applications in the fields of genomics and molecular diagnostics for improving patient care.
Artificial Intelligence in Cancer Genomics and Therapy
Olivier Elemento, PhD, Weill Cornell Medicine - Englander Institute for Precision Medicine, New York, NY, USA
Probabilistic Graphical Models for Integrative Analysis of Pathomics Data
Panagiotis Benos, PhD, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
Objectives:
- Describe the concepts of artificial intelligence and machine learning.
- Summarize the emergence of big data in genomics, including the challenges and opportunities associated with large-scale analyses.
- Examine the current and future use of these tools in the genomics and molecular pathology practice.
Duration: 1.50 hr
Recording Date: November 3, 2018
Last day to purchase: December 31, 2021
There are no CME/CMLE or SAMs credits available.Note: Join the AMP Family for discounted access to the most current educational resources!
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