A Data Warehousing Approach for Genomics Data Meta-Analysis - Evolving Application Domains of Data Warehousing and Mining - page 149

Figure 5. Cluster_GSE6281_hclust_2 excerpt

Figure 6. Most frequent GO annotations in Cluster_GSE6281_hclust_1

Figure 7. Most frequent GO annotations in Cluster_GSE6281_hclust_2

MeatOnto biomedical publications and we developed two

new ontologies: GEOnto for experiments and
conditions, and GMineOnto to annotate statistical
and mining results on numeric experimental data.
These annotations provide descriptive metadata
stored in the AMI knowledge base (represented
by (3) in Figure 1).

MeatOnto (Khelif et al., 2007) is devoted to con-
cepts related to biomedical literature resources.
Briefly, it is based on two sub-ontologies: UMLS
semantic network that integrates the Gene Ontol-
ogy enriched by more specific relations to describe
the biomedical domain (biomedical concepts and
their interrelations), and DocOnto that describes