The application of various clustering techniques for large-scale gene-expression measurement experiments is a well-established method in bioinformatics. Clustering is also usually accompanied by functional characterization of gene sets by assessing statistical enrichments of structured vocabularies, such as the gene ontology (GO) [Gene Ontology Consortium. The gene ontology (GO) project in 2006. Nucleic Acids Research (Database issue), vol. 34; 2006. p. D322-6]. If different clusters are generated for correlated experiments, a machine learning step termed cluster meta-analysis may be performed, in order to discover relations among the components of such sets. Several approaches have been proposed: in particular, kernel methods may be used to exploit the graphical structure of typical ontologies such as GO. Following up the formulation of such approach [Merico D, Zoppis I, Antoniotti M, Mauri G. Evaluating graph kernel methods for relation discovery in GO-annotated clusters. In: KES-2007/WIRN-2007, Part IV, Lecture notes in artificial intelligence, vol. 4694. Berlin: Springer; 2007. p. 892-900; Zoppis I, Merico D, Antoniotti M, Mishra B, Mauri G. Discovering relations among GO-annotated clusters by graph kernel methods. In: Proceedings of the 2007 international symposium on bioinformatics research and applications. Lecture notes in computer science, vol. 4463. Berlin: Springer; 2007], in this paper we discuss, from an information-theoretic point of view, further results about its applicability and its performance.

Antoniotti, M., Carreras, M., Farinaccio, A., Mauri, G., Merico, D., & Zoppis, I. (2010). An Application of Kernel Methods to Gene Cluster Temporal Meta-Analysis. COMPUTERS & OPERATIONS RESEARCH, 37(8), 1361-1368 [10.1016/j.cor.2009.03.011].

An Application of Kernel Methods to Gene Cluster Temporal Meta-Analysis

ANTONIOTTI, MARCO;FARINACCIO, ANTONELLA;MAURI, GIANCARLO;ZOPPIS, ITALO FRANCESCO
2010

Abstract

The application of various clustering techniques for large-scale gene-expression measurement experiments is a well-established method in bioinformatics. Clustering is also usually accompanied by functional characterization of gene sets by assessing statistical enrichments of structured vocabularies, such as the gene ontology (GO) [Gene Ontology Consortium. The gene ontology (GO) project in 2006. Nucleic Acids Research (Database issue), vol. 34; 2006. p. D322-6]. If different clusters are generated for correlated experiments, a machine learning step termed cluster meta-analysis may be performed, in order to discover relations among the components of such sets. Several approaches have been proposed: in particular, kernel methods may be used to exploit the graphical structure of typical ontologies such as GO. Following up the formulation of such approach [Merico D, Zoppis I, Antoniotti M, Mauri G. Evaluating graph kernel methods for relation discovery in GO-annotated clusters. In: KES-2007/WIRN-2007, Part IV, Lecture notes in artificial intelligence, vol. 4694. Berlin: Springer; 2007. p. 892-900; Zoppis I, Merico D, Antoniotti M, Mishra B, Mauri G. Discovering relations among GO-annotated clusters by graph kernel methods. In: Proceedings of the 2007 international symposium on bioinformatics research and applications. Lecture notes in computer science, vol. 4463. Berlin: Springer; 2007], in this paper we discuss, from an information-theoretic point of view, further results about its applicability and its performance.
No
Articolo in rivista - Articolo scientifico
Scientifica
Bioinformatics; Gene Clustering, Kernel methods, Enrichment Studies
English
1361
1368
8
Antoniotti, M., Carreras, M., Farinaccio, A., Mauri, G., Merico, D., & Zoppis, I. (2010). An Application of Kernel Methods to Gene Cluster Temporal Meta-Analysis. COMPUTERS & OPERATIONS RESEARCH, 37(8), 1361-1368 [10.1016/j.cor.2009.03.011].
Antoniotti, M; Carreras, M; Farinaccio, A; Mauri, G; Merico, D; Zoppis, I
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/8615
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