The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Online Program Home
Abstract Details
|
Activity Number:
|
659
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #304099 |
|
Title:
|
Multivariate Regression Hidden Markov Models
|
|
Author(s):
|
Yeonok Lee*+
|
|
Companies:
|
|
|
Address:
|
455 Waupelani Drive, State College, PA, 16861-4412, United States
|
|
Keywords:
|
Hidden Markov model ;
Multivariate regression
|
|
Abstract:
|
Hidden Markov models (HMMs) are widely applied in Genomes and Genomics. Genetic analysis often involves correctly labeling genetic regions: exons or introns and promoters or enhancer, etc. Nowadays, advances in sequencing technology allow researchers to combine many biological information, such as chromatin states, gene expression levels, and mitochondrial effects, which can aid to better understand underlying biological networks. For example, it is known that chromatin modification is associated with gene expression. Searching for the relationship, and segregating the genomic regions at the same time, we developed variant HMMs, in which observations have a (generalised) linear relationship. We conclude that a simulation study result, using a logistic regression model, is a promising approach to identify the hidden state(label).
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2012 program
|
2012 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.