Mining biomarker information in biomedical literature

Abstract

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.

Systematic Analysis of GWAS Data Reveals Genomic Hotspots for Shared Mechanisms between Neurodegenerative Diseases

“…In order to investigate, whether there are any overlapping genome stretches between the 'loci of shared GWAS and LD genetic variants' and 'loci of the well-established disease-associated genes in the literature'; in addition to the datadriven approaches described above, a comprehensive knowledge driven approach was also conducted, by searching systematically from literature with the help of a literature mining environment-SCAIView [24].…”

A network model of genomic hormone interactions underlying dementia and its translational validation through serendipitous off-target effect

“…For retrieval and extraction of putative biomarker information from the literature, biomarker terminology was used [56]. Pathway membership for each target was obtained from KEGG database [57] and their association to disease was determined using genetic association database [58].…”

Application of text mining in the biomedical domain

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