Name
Abstract | ||
|---|---|---|
The biological interpretation of large-scale gene expression data is one of the challenges in current bioinformatics. The state-of-theart 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 cdc15- subset of the well known Spellman's Yeast Cell Cycle dataset [2]. |
Year | Venue | Keywords |
|---|---|---|
2007 | ISBRA | go-annotated cluster,current bioinformatics,yeast cell cycle dataset,specific flat list method,biological interpretation,cluster meta-analysis,graph kernel method,flat list,discovering relation,gene ontology term,state-of-theart approach,kernel approach,different cluster,meta analysis,machine learning,gene expression,bioinformatics,cell cycle,kernel method,kernel methods |
Field | DocType | Volume |
Kernel (linear algebra),Graph kernel,Data mining,Cluster (physics),Graph,Computer science,Gene ontology,Exploit,Artificial intelligence,Bioinformatics,Cluster analysis,Machine learning | Conference | 4463 |
ISSN | Citations | PageRank |
0302-9743 | 3 | 0.43 |
References | Authors | |
14 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Italo Zoppis | 1 | 38 | 18.39 |
| Daniele Merico | 2 | 13 | 3.19 |
| Marco Antoniotti | 3 | 102 | 18.50 |
| Bud Mishra | 4 | 1368 | 219.91 |
| Giancarlo Mauri | 5 | 2106 | 297.38 |