Genomics | Bioinformatics | Medical Genetics
Mining PubMed for biomarker-disease associations to guide discovery
Walter J Jessen*, Katherine T Landschulz, Thomas G Turi, Rachel Y Reams
*Corresponding author: Walter J Jessen
Covance Biomarker Center of Excellence, Discovery and Translational Services, Greenfield, IN, USA
F1000Posters 2012, 3: 124 (poster) [English]
Poster [5.42 MB]
19th Annual Molecular Medicine Tri-Conference 2012, 19 - 23 Feb 2012, 20
Biomedical knowledge is growing exponentially; however, meta-knowledge around the data is often lacking. PubMed is a database comprising more than 21 million citations for biomedical literature from MEDLINE and additional life science journals dating back to the 1950s. To explore the use and frequency of biomarkers across human disease, we mined PubMed for biomarker-disease associations. We then ranked the top 100 linked diseases by relevance and mapped them to medical subject headings (MeSH) and, subsequently, to the Disease Ontology. To identify biomarkers for each disease, we queried Covance BioPathways, an online data resource that maps commercial biomarker assays to biological and disease pathways. We then integrated pathways-based information to describe both known and potential biomarkers, as well as disease-associated genes/proteins for select diseases (atherosclerosis and asthma). This approach identifies therapeutic areas with candidate or validated biomarkers, and highlights those areas where a paucity of biomarkers exists.
Given the molecular interdependencies within a cell, a disease is rarely a consequence of a single gene abnormality, but instead reflects the perturbation of a complex network of biological and signaling pathways. The approach outlined here describes the detection and ranking of human disease based on research/clinical activity surrounding biomarkers. It also enables the identification of therapeutic areas with candidate or validated biomarkers. The strategy takes an integrative approach to identify candidate disease biomarkers by combining disease-associated genes/proteins with commercially validated assays for known biomarkers. We first constructed a system-level model of disease that incorporates molecular interactions across biological and signaling pathways. We then identified each gene/protein in the model that has an existing commercially validated assay. This research offers an alternative, comprehensive view of key relationships and pathway perturbations that may identify biomarkers of disease emergence or progression.
No relevant competing interests disclosed.
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