Journal
BRIEFINGS IN BIOINFORMATICS
Volume 13, Issue 4, Pages 460-494Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbs018
Keywords
text mining; information extraction; knowledge discovery from texts; text analytics; biomedical natural language processing; pharmacogenomics; pharmacogenetics
Funding
- German Ministry of Education and Research (BMBF) as part of the National Research Core within the Jena Centre of Systems Biology of Ageing (JENAGE) [0315581D]
- NIH [5R01 LM009254-06, 5R01 LM008111-07, 5R01 GM083649-04, U54-HG004028]
- National Institutes of Health [GM61374]
- National Library of Medicine [HHSN276201000025C]
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This article surveys efforts on text mining of the pharmacogenomics literature, mainly from the period 2008 to 2011. Pharmacogenomics (or pharmacogenetics) is the field that studies how human genetic variation impacts drug response. Therefore, publications span the intersection of research in genotypes, phenotypes and pharmacology, a topic that has increasingly become a focus of active research in recent years. This survey covers efforts dealing with the automatic recognition of relevant named entities (e.g. genes, gene variants and proteins, diseases and other pathological phenomena, drugs and other chemicals relevant for medical treatment), as well as various forms of relations between them. A wide range of text genres is considered, such as scientific publications (abstracts, as well as full texts), patent texts and clinical narratives. We also discuss infrastructure and resources needed for advanced text analytics, e.g. document corpora annotated with corresponding semantic metadata (gold standards and training data), biomedical terminologies and ontologies providing domain-specific background knowledge at different levels of formality and specificity, software architectures for building complex and scalable text analytics pipelines and Web services grounded to them, as well as comprehensive ways to disseminate and interact with the typically huge amounts of semiformal knowledge structures extracted by text mining tools. Finally, we consider some of the novel applications that have already been developed in the field of pharmacogenomic text mining and point out perspectives for future research.
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