Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 12:00 PM ET (12:00 - 16:00 UTC). We apologize for the inconvenience.
By Topic

Empirical mode decomposition as a tool for DNA sequence analysis from terahertz spectroscopy measurements

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Weng, B. ; Dept. of Electr. & Comput. Eng., Delaware Univ., Newark, DE ; Guangchi Xuan ; Kolodzey, J. ; Barner, K.E.

DNA sequence analysis has been widely studied by gene-expression microarray techniques. Few results, however, have been provided by Terahertz spectroscopy which reveals the absorbtion or reflectance percentage from different DNA sequences. Previous Terahertz methods have lacked a quantitative analysis of the spectroscopy features, and no definitive conclusion regarding the data can be easily drawn. In this paper, we use a signal processing approach which gives a quantitative interpretation of the DNA spectroscopy. Due to the presence of physical noise, the data can be contaminated by both random fluctuations and impulsive noise. A new signal processing tool called empirical mode decomposition (EMD) is employed to remove the noise and extract the trend of the signal. The data is subsequently partitioned by clustering methods. Experimental results of Terahertz spectroscopy of several different DNA samples show that the EMD aids the clustering process and yields clustering of higher validity than that obtained from the raw data.

Published in:

Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on

Date of Conference:

28-30 May 2006