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Activity Number: 40 - Statistical Methods for Microbiome and Tumor Data
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #306775
Title: Predictive Models for Detecting Association Between MiRNAs and Lympho Vascular Invasion
Author(s): Moumita Karmakar* and Pei-chun Lai and Samiran Sinha and Sanjukta Chakraborty
Companies: Texas A&M University and Texas A&M University and Texas A&M University and Texas A&M University
Keywords: cancer; cluster; lympho vascular invasion; miRNA; prediction; supervised clustering

Head-Neck Squamous Cell Carcinoma is one of the most common types of cancer among older people. The Collective cancer known as Head-Neck usually begins in squamous cell and account for approximately 4% of all cancers in the United States. In this work, our main goal is to detect the significant miRNAs and how their expression level gets affected by lymphovascular invasion, considered as a strong and independent predictor of several other cancer types. To better understand the connection, we first cluster the genes and then develop a summary measure for each cluster. We use the cluster information to develop a predictive model and obtain connection among survival status, lymphovascular invasion, and miRNA expressions. We implement our algorithm on the Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma (TCGA-HNSC) data.

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

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