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Dadun > Depósito Académico > CIMA (Centro de Investigación Médica Aplicada) > Área de Terapia génica y Hepatología > Hepatología bioquímica > DA - CIMA - Terapia génica y Hepatología - Hepatología bioquímica - Artículos de revista >

Correlation between gene expression and GO semantic similarity
Autor(es) : Sevilla, J.L. (José L.)
Segura, V. (Víctor)
Podhorski, A. (Adam)
Guruceaga, E. (Elizabeth)
Mato, J.M. (José María)
Martinez-Cruz, L.A. (L. Alfonso)
Corrales, F.J. (Fernando José)
Rubio, A. (Ángel)
Palabras clave : Computational Biology/methods
Gene Expression
Fecha incorporación: 2005
Editorial : Institute of Electrical and Electronics Engineers
Versión del editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1541985
ISSN: 1557-9964
Cita: Sevilla JL, Segura V, Podhorski A, Guruceaga E, Mato JM, Martinez-Cruz LA, et al. Correlation between gene expression and GO semantic similarity. IEEE/ACM Trans Comput Biol Bioinform 2005 Oct-Dec;2(4):330-338.
Resumen
This research analyzes some aspects of the relationship between gene expression, gene function, and gene annotation. Many recent studies are implicitly based on the assumption that gene products that are biologically and functionally related would maintain this similarity both in their expression profiles as well as in their Gene Ontology (GO) annotation. We analyze how accurate this assumption proves to be using real publicly available data. We also aim to validate a measure of semantic similarity for GO annotation. We use the Pearson correlation coefficient and its absolute value as a measure of similarity between expression profiles of gene products. We explore a number of semantic similarity measures (Resnik, Jiang, and Lin) and compute the similarity between gene products annotated using the GO. Finally, we compute correlation coefficients to compare gene expression similarity against GO semantic similarity. Our results suggest that the Resnik similarity measure outperforms the others and seems better suited for use in Gene Ontology. We also deduce that there seems to be correlation between semantic similarity in the GO annotation and gene expression for the three GO ontologies. We show that this correlation is negligible up to a certain semantic similarity value; then, for higher similarity values, the relationship trend becomes almost linear. These results can be used to augment the knowledge provided by clustering algorithms and in the development of bioinformatic tools for finding and characterizing gene products.
Enlace permanente: http://hdl.handle.net/10171/21565
Aparece en las colecciones: DA - CIMA - Unidad de Proteómica, Genómica y Bioinformática - Artículos de revista
DA - CIMA - Terapia génica y Hepatología - Hepatología bioquímica - Artículos de revista

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