title: Linear predictive coding and wavelet decomposition for robust microarray data clustering.
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. Genomic signal processing is a new area of research that combines genomics with digital signal processing methodologies. In this paper, we present a comparative analysis of two genomic signal processing methods namely Linear Predictive Coding and Discrete Wavelet Decomposition for robust microarray data clustering. Vector quantization is applied to the resultant coefficients to provide the clustering of the data samples. Both techniques were validated for standard data sets. Comparative analyses of the results indicate that these methods provide improved clustering accuracy compared to some conventional clustering techniques. Moreover, there classifiers don't require any prior training procedures.
authors.affiliation: Mobile Information and Network Technologies Research Centre, Kingston University, London, KT1 2EE. r.istepanian@kingston.ac.uk
authors.forename: Robert S H
authors.initials: RS
authors.lastname: Istepanian
authors.forename: Ala
authors.initials: A
authors.lastname: Sungoor
authors.forename: Jean-Christophe
authors.initials: JC
authors.lastname: Nebel
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012680:Sensitivity and Specificity; D012984:Software