Dataset Information

Mapping biomedical concepts onto the human genome by mining literature on chromosomal aberrations.

ABSTRACT: Biomedical literature provides a rich but unstructured source of associations between chromosomal regions and biomedical concepts. By mining MEDLINE abstracts, we annotate the human genome at the level of cytogenetic bands. Our method creates a set of chromosomal aberration maps that associate cytogenetic bands to biomedical concepts from a variety of controlled vocabularies, including disease, dysmorphology, anatomy, development and Gene Ontology branches. The association between a band (e.g. 4p16.3) and a concept (e.g. microcephaly) is assessed by the statistical overrepresentation of this concept in the abstracts relating to this band. Our method is validated using existing genome annotation resources and known chromosomal aberration maps and is further illustrated through a case study on heart disease. Our chromosomal aberration maps provide diagnostics support to clinical geneticists, aid cytogeneticists to interpret and report cytogenetic findings and support researchers interested in human gene function. The method is available as a web application, aBandApart, at http://www.esat.kuleuven.be/abandapart/.

SUBMITTER: Van Vooren S

PROVIDER: S-EPMC1885641 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Mapping biomedical concepts onto the human genome by mining literature on chromosomal aberrations.

Van Vooren Steven S Thienpont Bernard B Menten Björn B Speleman Frank F De Moor Bart B Vermeesch Joris J Moreau Yves Y

Nucleic acids research 20070401 8

Biomedical literature provides a rich but unstructured source of associations between chromosomal regions and biomedical concepts. By mining MEDLINE abstracts, we annotate the human genome at the level of cytogenetic bands. Our method creates a set of chromosomal aberration maps that associate cytogenetic bands to biomedical concepts from a variety of controlled vocabularies, including disease, dysmorphology, anatomy, development and Gene Ontology branches. The association between a band (e.g. 4 ...[more]

PMID: 17403693

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Project description:BackgroundFor selection and evaluation of potential biomarkers, inclusion of already published information is of utmost importance. In spite of significant advancements in text- and data-mining techniques, the vast knowledge space of biomarkers in biomedical text has remained unexplored. Existing named entity recognition approaches are not sufficiently selective for the retrieval of biomarker information from the literature. The purpose of this study was to identify textual features that enhance the effectiveness of biomarker information retrieval for different indication areas and diverse end user perspectives.MethodsA biomarker terminology was created and further organized into six concept classes. Performance of this terminology was optimized towards balanced selectivity and specificity. The information retrieval performance using the biomarker terminology was evaluated based on various combinations of the terminology's six classes. Further validation of these results was performed on two independent corpora representing two different neurodegenerative diseases.ResultsThe current state of the biomarker terminology contains 119 entity classes supported by 1890 different synonyms. The result of information retrieval shows improved retrieval rate of informative abstracts, which is achieved by including clinical management terms and evidence of gene/protein alterations (e.g. gene/protein expression status or certain polymorphisms) in combination with disease and gene name recognition. When additional filtering through other classes (e.g. diagnostic or prognostic methods) is applied, the typical high number of unspecific search results is significantly reduced. The evaluation results suggest that this approach enables the automated identification of biomarker information in the literature. A demo version of the search engine SCAIView, including the biomarker retrieval, is made available to the public through http://www.scaiview.com/scaiview-academia.html.ConclusionsThe approach presented in this paper demonstrates that using a dedicated biomarker terminology for automated analysis of the scientific literature maybe helpful as an aid to finding biomarker information in text. Successful extraction of candidate biomarkers information from published resources can be considered as the first step towards developing novel hypotheses. These hypotheses will be valuable for the early decision-making in the drug discovery and development process.

Dataset Information

Mapping biomedical concepts onto the human genome by mining literature on chromosomal aberrations.

Publications

Mapping biomedical concepts onto the human genome by mining literature on chromosomal aberrations.

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