Abstract #301274

This is the preliminary program for the 2003 Joint Statistical Meetings in San Francisco, California. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 2-5, 2003); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

Back to main JSM 2003 Program page
JSM 2003 Abstract #301274
Activity Number: 288
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #301274
Title: Hidden Markov Model and Gene Expression
Author(s): Ernst Wit*+ and John McClure and Nial Friel
Companies: University of Glasgow and University of Glasgow and University of Glasgow
Address: Department of Statistics, Glasgow, G12 8QW, England
Keywords: Hidden Markov Models ; gene expression ; microarrays ; normalizing constant ; pseudolikelihood ; genetics
Abstract:

Microarray technology has made the simultaneous measurement of gene transcription a routine activity. Whereas gene transcription is only one stage in the complex genomic process of living organisms, it gives a fascinating insight in one aspect of this activity across the whole genome. Gene regulation is a complex biological process, which involves gene-gene and gene-protein interactions. An operator region, to which the enzyme polymerase can bind to start transcription, precedes the gene sequence. Such local features regulating transcription, pose the question whether there might be local spatial gene interactions. We define a Hidden Markov Model (HMM) to relate the observed expression levels to hidden states "Up", "Down" and "Same" for a time-series gene expression dataset. A Potts Model is identified to describe the interactions between neighboring states. A typical problem in these types of model is the estimation of the hidden parameters because of the intractability of the normalizing constant. Recent work by Pettitt et al. (2002) provides a clue to avoid to use pseudolikehood and to solve this issue for a wide class of HMMs.

  • 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 2003 program

JSM 2003 For information, contact meetings@amstat.org or phone (703) 684-1221. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2003