title: Biomarker identification using text mining.
abstract: Identifying molecular biomarkers has become one of the important tasks for scientists to assess the different phenotypic states of cells or organisms correlated to the genotypes of diseases from large-scale biological data. In this paper, we proposed a text-mining-based method to discover biomarkers from PubMed. First, we construct a database based on a dictionary, and then we used a finite state machine to identify the biomarkers. Our method of text mining provides a highly reliable approach to discover the biomarkers in the PubMed database.
authors.affiliation: Department of Systems and Computer Science, Howard University, Washington, DC 20059, USA. hli3302@gmail.com
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.forename: Chunmei
authors.initials: C
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D030342:Genetic Diseases, Inborn / Q000378:metabolism; D005838:Genotype; D006801:Humans; D010641:Phenotype; D011381:Programming Languages; D039781:PubMed
chemical_list: D015415:Biomarkers