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Activity Number: 176 - Bayesian Mixture Modeling, Clustering and Unsupervised Learning
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #305285 Presentation
Title: Nonparametric Bayesian Functional Clustering for Breast Cancer Disparities
Author(s): Wenyu Gao* and Wonil Nam and Inyoung Kim and Wei Zhou
Companies: Virginia Tech and Bradley Department of Electrical and Computer Engineering, Virginia Tech and Virginia Tech and Bradley Department of Electrical and Computer Engineering, Virginia Tech
Keywords: Breast Cancer Disparities; Functional Clustering; SERS; WDPM

It has been found that different incidence and mortality rates for breast cancer exist among various racial populations. For instance, Caucasian women are more likely to develop breast cancer than African American women. To study these disparities, surface-enhanced Raman spectroscopy (SERS) has been conducted to provide biomolecular fingerprint information. Extracellular SERS signals from each cell type were measured by a practical high-performance SERS device. However, large intraclass variations exist due to cellular and additional cancerous heterogeneity. To study the differences between two types of triple negative breast cancer cell lines at the molecular level, we performed clustering analyses on the massive nonlinear curves of signals versus Raman shifts. In this paper, we proposed a nonparametric Bayesian functional clustering method via Weighted Dirichlet Process Mixture (WDPM) modeling, which clusters automatically and determines correct number of clusters. Based on our analyses, we identified that the clustering behaviors vary across different racial groups. This clustering information will be useful to further investigate health disparities among groups.

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

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