TY - GEN
T1 - Discovering relations among GO-annotated clusters by graph kernel methods
AU - Zoppis, Italo
AU - Merico, Daniele
AU - Antoniotti, Marco
AU - Mishra, Bud
AU - Mauri, Giancarlo
PY - 2007
Y1 - 2007
N2 - 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].
AB - 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].
UR - http://www.scopus.com/inward/record.url?scp=34547491727&partnerID=8YFLogxK
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U2 - 10.1007/978-3-540-72031-7_15
DO - 10.1007/978-3-540-72031-7_15
M3 - Conference contribution
AN - SCOPUS:34547491727
SN - 3540720308
SN - 9783540720300
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 158
EP - 169
BT - Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings
PB - Springer Verlag
T2 - 3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007
Y2 - 7 May 2007 through 10 May 2007
ER -