Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text

Garten, Yael; Altman, Russ B.

doi:10.1186/1471-2105-10-s2-s6

Cited by 107 publications

(77 citation statements)

References 17 publications

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“…Some attempts have been made for PGx relationship extraction. For example, Garten and Altman developed an ontology-based tool, Pharmspresso, for extracting PGx information from full text articles by identifying concepts (such as genes, drugs, polymorphisms, and diseases) and relationships (such as action, association and comparison) [24]. Ahlers et al developed a rulebased method for extracting specific PGx relationships such as ‘genetic etiology’ and ‘pharmacological effects’ from PubMed abstracts [25].…”

Section: Related Workmentioning

confidence: 99%

Systematic identification of pharmacogenomics information from clinical trials

2012

Journal of Biomedical Informatics

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Recent progress in high-throughput genomic technologies has shifted pharmacogenomic research from candidate gene pharmacogenetics to clinical pharmacogenomics (PGx). Many clinical related questions may be asked such as ‘what drug should be prescribed for a patient with mutant alleles?’ Typically, answers to such questions can be found in publications mentioning the relationships of the gene–drug–disease of interest. In this work, we hypothesize that ClinicalTrials.gov is a comparable source rich in PGx related information. In this regard, we developed a systematic approach to automatically identify PGx relationships between genes, drugs and diseases from trial records in ClinicalTrials.gov. In our evaluation, we found that our extracted relationships overlap significantly with the curated factual knowledge through the literature in a PGx database and that most relationships appear on average 5 years earlier in clinical trials than in their corresponding publications, suggesting that clinical trials may be valuable for both validating known and capturing new PGx related information in a more timely manner. Furthermore, two human reviewers judged a portion of computer-generated relationships and found an overall accuracy of 74% for our text-mining approach. This work has practical implications in enriching our existing knowledge on PGx gene–drug–disease relationships as well as suggesting crosslinks between ClinicalTrials.gov and other PGx knowledge bases.

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Section: Related Workmentioning

confidence: 99%

Systematic identification of pharmacogenomics information from clinical trials

2012

Journal of Biomedical Informatics

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“…Le système d'extraction doit donc analyser les articles complets. Peu de systèmes traitent d'articles complets, et ceux qui existent, comme BioRAT (Corney et al, 2004) et Pharmspresso (Garten et al, 2009), extraient des informations en relation au sein d'une même phrase.…”

Section: Introductionunclassified

Extraction de relations complexes. Application à des résultats expérimentaux en physiologie rénale

Minard¹,

Grau²,

Ligozat³

et al. 2013

Techniques et sciences informatiques

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“…There are a number of Textpresso web servers that have been developed by groups in a variety of fields (International Arabidopsis Informatics Consortium, 2010;Gama--Castro et al, 2008;Garten and Altman, 2009;Harris et al, 2010;Muller et al, 2008;Shen--Orr et al, 2009;Skrzypek et al, 2010;Urbanski and Condie, 2009;Yamazaki et al, 2010). Textpresso uses ontologies to index sentences contained within papers to enable the discovery of linkages between concepts that may not be explicitly expressed in the body or abstract.…”

Section: Introduction To Textpressomentioning

confidence: 99%

Apellicon: a web-based tool for constructing and curating Textpresso databases.

Urbanski

Condie

2010

Nat Prec

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As more research literature in the biological sciences is made available in electronic format, text mining systems are increasingly being used to improve the ability of investigators to retrieve relevant information. Through the use of advanced indexing techniques that utilize biological ontologies, semantic databases, and other formal representations of biological concepts text mining systems have been able to effectively parse biological literature. While text mining systems are increasingly effective at creating the linkages required to provide context--specific search results, the systems themselves are difficult to set up and use by novice computer users due to the highly technical nature of the applications. Because most researchers in the biological sciences do not have a strong computer science background we have focused on improving the quality of existing, proven text mining systems by implementing a web--based GUI that greatly improves the workflow of these systems. Textpresso in particular has an excellent web--based interface for searching literature but does not have an easy to use administrative interface. We developed the Apellicon interface to enable a wide range of users to build and manage a Textpresso database. An important feature of Apellicon is that it can enable groups to collaborate in building a Textpresso database.

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Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text

Cited by 107 publications

References 17 publications

Systematic identification of pharmacogenomics information from clinical trials

Systematic identification of pharmacogenomics information from clinical trials

Extraction de relations complexes. Application à des résultats expérimentaux en physiologie rénale

Apellicon: a web-based tool for constructing and curating Textpresso databases.

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