Application of text mining in the biomedical domain

• Published in: Methods (San Diego, Calif.) Calif.), 2015-03, Vol.74, p.97-106
• Main Authors: Fleuren, Wilco W.M., Alkema, Wynand
• Format: Article
• Language: English
• Subjects: Animals; Automatic information extraction; Biomedical research; Biomedical Research - methods; Biomedical Research - statistics & numerical data; Data Mining - methods; Drug discovery; Humans; Natural language processing; Ontology; Periodicals as Topic - statistics & numerical data; Text mining
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2015.01.015

•The number of biomedical data and research papers grows exponentially.•We give an overview of how text mining is used to extract meaningful facts from these data.•Text mining workflows consist of information retrieval, information extraction and knowledge discovery.•The underlying principles of text mining are explained.•Application to biomedical use cases is discussed. In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for.