Title :
Linear Predictive Coding for Enhanced Microarray Data Clustering
Author :
Istepanian, Robert S H ; Sungoor, Ala ; Nebel, Jean-Christophe
Author_Institution :
Kingston Univ. Kingston-Upon-Thames, Kingston upon Thames
Abstract :
Microarrays are powerful tools for simultaneous monitoring of the expression levels of large number of genes. Their analysis is usually achieved by using clustering techniques. In this paper, we present a new clustering method based on Linear Predictive Coding to provide enhanced microarray data analysis. In this approach, spectral analysis of microarray data is performed to classify samples according to their distortion values. The technique was validated for a standard data set. Comparative analysis of the results indicates that this method provides improved clustering accuracy compared to some conventional clustering techniques. Moreover, our classifier does not require any prior training procedure.
Keywords :
diseases; genetic engineering; genetics; linear predictive coding; pattern clustering; array data clustering enhancement; linear predictive coding; microarray data analysis; parative analysis; Bioinformatics; Cardiovascular diseases; Data analysis; Data mining; Distortion measurement; Gene expression; Genomics; Linear predictive coding; Signal processing; Spectral analysis;
Conference_Titel :
Genomic Signal Processing and Statistics, 2007. GENSIPS 2007. IEEE International Workshop on
Conference_Location :
Tuusula
Print_ISBN :
978-1-4244-0998-3
Electronic_ISBN :
978-1-4244-0999-0
DOI :
10.1109/GENSIPS.2007.4365815