Discovering relations among GO-annotated clusters by graph kernel methods

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

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


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
PublisherSpringer Verlag
Number of pages12
ISBN (Print)3540720308, 9783540720300
StatePublished - 2007
Event3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007 - Atlanta, GA, United States
Duration: May 7 2007May 10 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4463 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007
Country/TerritoryUnited States
CityAtlanta, GA

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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