Mining PubMed for biomarker-disease associations to guide discovery
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Biology, Cardiology, Evidence Based Medicine, Immunology
- Keywords
- asthma, biomarker, text mining, pubmed, atherosclerosis, disease association, biomarker assay, biomarker discovery, mesh
- Copyright
- © 2015 Jessen et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. Mining PubMed for biomarker-disease associations to guide discovery. PeerJ PrePrints 3:e1446v1 https://doi.org/10.7287/peerj.preprints.1446v1
Abstract
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. This approach identifies therapeutic areas with candidate or validated biomarkers, and highlights those areas where a paucity of biomarkers exists.
Author Comment
This is a preprint submission to PeerJ.