Title :
Mining disease associated biomarker networks from PubMed
Author_Institution :
Sch. of Inf. Sci. & Technol., Drexel Univ., Philadelphia, PA, USA
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;
Conference_Titel :
Systems Biology (ISB), 2013 7th International Conference on
Conference_Location :
Huangshan
DOI :
10.1109/ISB.2013.6623786
