An information theoretic approach for analyzing temporal patterns of gene expression.
Bioinformatics
; 19(4): 449-58, 2003 Mar 01.
Article
en En
| MEDLINE
| ID: mdl-12611799
ABSTRACT
MOTIVATION Arrays allow measurements of the expression levels of thousands of mRNAs to be made simultaneously. The resulting data sets are information rich but require extensive mining to enhance their usefulness. Information theoretic methods are capable of assessing similarities and dissimilarities between data distributions and may be suited to the analysis of gene expression experiments. The purpose of this study was to investigate information theoretic data mining approaches to discover temporal patterns of gene expression from array-derived gene expression data. RESULTS:
The Kullback-Leibler divergence, an information-theoretic distance that measures the relative dissimilarity between two data distribution profiles, was used in conjunction with an unsupervised self-organizing map algorithm. Two published, array-derived gene expression data sets were analyzed. The patterns obtained with the KL clustering method were found to be superior to those obtained with the hierarchical clustering algorithm using the Pearson correlation distance measure. The biological significance of the results was also examined.AVAILABILITY:
Software code is available by request from the authors. All programs were written in ANSI C and Matlab (Mathworks Inc., Natick, MA).
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Bases de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Regulación de la Expresión Génica
/
Alineación de Secuencia
/
Análisis de Secuencia de ADN
/
Análisis de Secuencia por Matrices de Oligonucleótidos
/
Perfilación de la Expresión Génica
Tipo de estudio:
Evaluation_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2003
Tipo del documento:
Article
País de afiliación:
Estados Unidos