Biological knowledge extraction a case study of ihop and other language processing systems

Abstract

Text Mining is the process of extracting [novel] interesting and non-trivial information and knowledge from unstructured text (Google™ search result for define: text mining). Information retrieval, natural language processing, information extraction, and text mining provide methodologies to shift the burden of tracing and relating data contained in text from the human user to the computer. The emergence of high-throughput techniques has allowed biosciences to switch its research focus on Systems Biology, increasing the demands on text mining and extraction of information from heterogeneous sources. This chapter will introduce the most fundamental uses of language processing methods in biology and present the basic resources openly available in the field. The search for information about a common disease, chronic myeloid leukemia, is used to exemplify the capabilities. Tools such as PubMed, eTBLAST, METIS, EBIMed, MEDIE, MarkerInfoFinder, HCAD, iHOP, Chilibot, and G2D - selected from a comprehensive list of currently available systems - provide users with a basic platform for performing complex operations on information accumulated in text.