title: The application of data mining to flow cytometry.
abstract: Data mining is the process of automating information discovery to detect useful patterns, correlations, and trends. Existing data must be fitted into a representative model from which useful information can be derived through a variety of algorithms. The routine generation of vast amounts of data make flow cytometry a logical target for the application of data mining. This informative unit discusses the steps of the data-mining process using the immunophenotyping of hematologic neoplasms to demonstrate the application. The author describes several types of algorithms and provides a useful resource list of commercially available tools.
authors.affiliation: University of Texas, Houston, Texas, USA.
authors.forename: Andy N D
authors.initials: AN
authors.lastname: Nguyen
mesh_terms: D000465:Algorithms; D001330:Electronic Data Processing / Q000379:methods*; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans