A symbolic computing approach to evidence code mapping for biological data integration and subjective analysis for reference associations for metabolic pathways

Abstract

Biological data are scattered across thousands of biological databases and hundreds of scientific journals. Integration among these databases faces numerous challenges including various levels of heterogeneity, limited accessibility, redundancy, and conflicts in the data. The integration process needs both quantitative and qualitative mechanisms to accommodate input metrics such as evidence, context, references, and experimental conditions, which are not uniform across the databases. Evidence codes reflect source reliability and data quality. However, different databases define their own evidence codes. This paper presents a mechanism to qualitatively integrate the evidence codes and the references specified by each database. The methodology is tested using a sample pathway from the BioCyc Tierl, KEGG, and MetNetDB pathway databases. The results are promising and form a concrete basis for data integration.