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Activity Number: 663 - Regression, Clustering and Gene Set Methods in Genomics
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #307141
Title: Integrating Pathway Information with Boosting to Construct a Sufficient Gene Set for Phenotype Classification
Author(s): Nusrat Jahan* and Huining Kang and Li Luo
Companies: James Madison University and University of New Mexico and University of New Mexico
Keywords: Boosting ; Sufficient dimension reduction; Sufficient gene set; RNA sequence; Pathway; AML

In this work, we propose a 2-step phenotype classification method based on functional genomic data. In the first step, pathways relevant to specific phenotype conditions are identified, then a sufficient dimension reduction (SDR) within each pathway is achieved. The SDR process identifies the most relevant sufficient sets of predictors from each pathway. In the second step, boosting – a machine learning approach is used to identify most significantly differential sets of sufficient pathway based predictors. We demonstrate our method using an RNA-sequencing dataset from a study of pediatric acute myeloid leukemia (AML).

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

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