Summary. The complete analysis of many large scale experiments requires the comparison of the produced output with data available in public databases. Because each datapbase uses its own nomenclature to classify entries, this task frequently implies the conversion of identifiers and, due to incomplete mapping between those identifiers, this tasks commonly causes loss of information. In this paper, we propose a methodology to improve the coverage of the mapping between database identifiers. As a starting point we use a local warehouse with the default mappings from the most relevant biological databases. Next we apply a methodology to four database identifiers (Ensembl, Entrez Gene, KEGG and UniProt). The results showed an improvement in the coverage of all relationships superior to 10% in three and to 7% in five relations.
['Joel P. Arrais', 'João E. Pereira', 'Pedro Lopes', 'Sérgio Matos', 'José Luís Oliveira']
Improving Cross Mapping in Biomedical Databases