By Topic

Identification of novel network components from temporal microarray profiles of malaria parasite

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

2 Author(s)
Hong Cai ; Dept. of Electr. Eng., Univ. of Texas at San Antonio, San Antonio, TX ; Agaian, S.S.

A significant roadblock to the use of genomic data for understanding gene networks in infectious pathogens is our inability to assign functionality to a large fraction of the genes. Nowhere is this more problematic than in the malaria parasite Plasmodium falciparum, in which 60% of the genes are annotated as "hypothetical". To circumvent this problem we proposed to employ wavelets, feature extraction, kernel based supervised learning, and pattern recognition algorithms to explore temporal expression profiles from the complex and dynamic developmental cycle in the parasite and discover crucial network components.

Published in:

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

Date of Conference:

28-30 May 2006