Discovering relations among GO-annotated clusters by graph kernel methods

Italo Zoppis, Daniele Merico, Marco Antoniotti, Bhubaneswar Mishra, Giancarlo Mauri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The biological interpretation of large-scale gene expression data is one of the challenges in current bioinformatics. The state-of-the-art approach is to perform clustering and then compute a functional characterization via enrichments by Gene Ontology terms [1]. To better assist the interpretation of results, it may be useful to establish connections among different clusters, This machine learning step is sometimes termed cluster meta-analysis, and several approaches have already been proposed; in particular, they usually rely on enrichments based on flat lists of GO terms, However, GO terms are organized in taxonomical graphs, whose structure should be taken into account when performing enrichment studies. To tackle this problem, we propose a kernel approach that can exploit such structured graphical nature. Finally, we compare our approach against a specific flat list method by analyzing the cdc.1.5-subset of the well known Spellman's Yeast Cell Cycle dataset [2].

Original languageEnglish (US)
Title of host publicationBioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings
Pages158-169
Number of pages12
Volume4463 LNBI
StatePublished - 2007
Event3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007 - Atlanta, GA, United States
Duration: May 7 2007May 10 2007

Other

Other3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007
CountryUnited States
CityAtlanta, GA
Period5/7/075/10/07

Fingerprint

Kernel Methods
Bioinformatics
Gene expression
Yeast
Cluster Analysis
Ontology
Learning systems
Genes
Cells
Gene Ontology
Term
Graph in graph theory
Computational Biology
Meta-Analysis
Cell Cycle
Yeasts
Gene Expression Data
Gene Expression
Machine Learning
Clustering

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Zoppis, I., Merico, D., Antoniotti, M., Mishra, B., & Mauri, G. (2007). Discovering relations among GO-annotated clusters by graph kernel methods. In Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings (Vol. 4463 LNBI, pp. 158-169)

Discovering relations among GO-annotated clusters by graph kernel methods. / Zoppis, Italo; Merico, Daniele; Antoniotti, Marco; Mishra, Bhubaneswar; Mauri, Giancarlo.

Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings. Vol. 4463 LNBI 2007. p. 158-169.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zoppis, I, Merico, D, Antoniotti, M, Mishra, B & Mauri, G 2007, Discovering relations among GO-annotated clusters by graph kernel methods. in Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings. vol. 4463 LNBI, pp. 158-169, 3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007, Atlanta, GA, United States, 5/7/07.
Zoppis I, Merico D, Antoniotti M, Mishra B, Mauri G. Discovering relations among GO-annotated clusters by graph kernel methods. In Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings. Vol. 4463 LNBI. 2007. p. 158-169
Zoppis, Italo ; Merico, Daniele ; Antoniotti, Marco ; Mishra, Bhubaneswar ; Mauri, Giancarlo. / Discovering relations among GO-annotated clusters by graph kernel methods. Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings. Vol. 4463 LNBI 2007. pp. 158-169
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