Mining disease associated biomarker networks from PubMed

Author

Zhong Huang

Author_Institution

Sch. of Inf. Sci. & Technol., Drexel Univ., Philadelphia, PA, USA

fYear

2013

fDate

23-25 Aug. 2013

Firstpage

15

Lastpage

18

Abstract

Disease related biomarker discovery is the critical step to realize the future personalized medicine and has been an important research area. With exponential growing of biomedical knowledge deposited in PubMed database, it is now an essential step to mine PubMed for biomarker-disease associations to support the laboratory research and clinical validation. We constructed list of human diseases that are most frequently associated with biomarker in literatures by text mining. Top ranked neurology diseases were then used to extract associated genes from PubMed using context sensitive information retrieval methods. Associated genes were then integrated into pathways and subject to network biomarker analysis. Our approach identifies both known and potential biomarkers for 3 neurodegenerative diseases.

Keywords

data mining; diseases; genetics; genomics; information retrieval; medical information systems; neurophysiology; PubMed database; biomarker discovery; biomarker network analysis; biomarker-disease association; biomedical knowledge; clinical validation; context sensitive information retrieval method; gene association extraction; laboratory research; neurodegenerative disease; personalized medicine; text mining; Biological system modeling; Diseases; Manuals; Proteins; Unified modeling language; biological network; biomarker; disease-gene association; semantic; text mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems Biology (ISB), 2013 7th International Conference on

Conference_Location

Huangshan

ISSN

2325-0704

Type

conf

DOI

10.1109/ISB.2013.6623786

Filename

6623786