title: In silico knowledge and content tracking.
abstract: This chapter gives a brief overview of text-mining techniques to extract knowledge from large text collections. It describes the basis pipeline of how to come from text to relationships between biological concepts and the problems that are encountered at each step in the pipeline. We first explain how words in text are recognized as concepts. Second, concepts are associated with each other using 2×2 contingency tables and test statistics. Third, we explain that it is possible to extract indirect links between concepts using the direct links taken from 2×2 table analyses. This we call implicit information extraction. Fourth, the validation techniques to evaluate a text-mining system such as ROC curves and retrospective studies are discussed. We conclude by examining how text information can be combined with other non-textual data sources such as microarray expression data and what the future directions are for text-mining within the Internet.
authors.affiliation: Department of Human Genetics, University Medical Center, Leiden, The Netherlands. hvanhaagen@gmail.com
authors.forename: Herman
authors.initials: H
authors.lastname: van Haagen
authors.forename: Barend
authors.initials: B
authors.lastname: Mons
mesh_terms: D019295:Computational Biology; D057225:Data Mining; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval; D020407:Internet; D015203:Reproducibility of Results; D012189:Retrospective Studies