Such systems rely on the mining of MEDLINE text [9], [10] or employ a similarity-based inference of disease genes [11], [12].
Winck et al. [ 23] obtained encouraging results by applying a context-based preprocessing to data mining of biological text.
The second most frequent identified application domain is the mining of web texts, comprising web pages, blogs, reviews, web forums, social medias, and email filtering [41 46].
Manual mining of free-text fields was performed in order to determine the most commonly used antimicrobials and common diagnoses.
This last step seems to be manageable, because these maps are generated by qualified biologists and are based on extensive mining of published texts.
Despite the use of extensive text-mining vocabularies, the ability of text mining to detect key words or terms and relationships in complex sentences with large separations between primary search terms, or studies that use non-standard keyword synonyms not seen in clinical QT reports means that some drugs will go undetected.
The conclusions are relevant for everyone who is developing mutation mining tools, probably most users of text mining algorithms, and they are well justified.
Research articles, review articles, clinical references, and practice guidelines are rich sources for text analytics applications that aim to discover knowledge by mining these type of text data.
In the second process, the full texts of papers are imported into Xenbase's Textpresso (3) text mining and search tool (see 'Use of text mining tools' section).
Note that 35% of the entries of our database were collected from text mining of MEDLINE abstracts and full text articles, which have not yet been collected or annotated by other public resources.
