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Activity Number: 322 - Data-Driven Patient Management in the Era of Precision Medicine - from Discovery, Diagnostics, to Therapeutics
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Council of Chapters
Abstract #326718
Title: Using Genomic Features to Make Smart Clinical Decisions: The Power of Machine Learning with RNA-Seq
Author(s): Jing Huang* and Su yeon Kim and Yangyang Hao and Jing Lu and Joshua Babiarz and Sean Walsh and Giulia Kennedy
Companies: Veracyte Inc and Veracyte Inc and Veracyte Inc and Veracyte Inc and Veracyte Inc and Veracyte Inc and Veracyte Inc
Keywords: machine learning; RNA-seq; genomic; diagnostic; clinical; sequencing

Machine learning and sequencing are two rapidly evolving fields: the former enables efficient processing of large data and accurate predictions; the latter provides information across the genome and allows researchers to study multiple samples simultaneously in a cost-effective manner. Here we use an example of thyroid nodule evaluation to illustrate how to apply advanced machine learning methods on sequencing data to develop clinically meaningful diagnostic tools. We built multi-layered classifiers using RNA sequencing derived from Fine Needle Aspirate (FNA) samples; they are minimally invasive, providing patient a safe and cost-effective diagnostic solution. It leverages deep domain knowledge and multiple types of genomic alterations such as differential gene expression, LOH, mitochondria content, fusions, and nucleotide variants to effectively differentiate benign from malignant nodules. In addition, we describe analytical solutions to challenges commonly faced in clinical settings such as small sample size, heterogeneity in patient populations, difficult subtypes, low sample quality and technical variability due to reagent/equipment induced batch effect.

Authors who are presenting talks have a * after their name.

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