Title :
Identification of novel network components from temporal microarray profiles of malaria parasite
Author :
Cai, Hong ; Agaian, Sos S.
Author_Institution :
Dept. of Electr. Eng., Univ. of Texas at San Antonio, San Antonio, TX
Abstract :
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.
Keywords :
data mining; diseases; feature extraction; genetics; learning (artificial intelligence); medical computing; microorganisms; pattern classification; wavelet transforms; data classification; feature extraction; gene network component; genomic data mining; infectious pathogen; kernel-based supervised learning; pattern recognition algorithm; plasmodium falciparum malaria parasite; temporal microarray expression profile; wavelet analysis; Bioinformatics; Biological system modeling; Diseases; Feature extraction; Fungi; Genomics; Kernel; Pathogens; Supervised learning; Wavelet analysis;
Conference_Titel :
Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on
Conference_Location :
College Station, TX
Print_ISBN :
1-4244-0384-7
Electronic_ISBN :
1-4244-0385-5
DOI :
10.1109/GENSIPS.2006.353138
