Wnt pathway curation using automated natural language processing: combining statistical methods with partial and full parse for knowledge extraction

Santos, Carlos; Eggle, Daniela; States, David J.

doi:10.1093/bioinformatics/bti165

Cited by 27 publications

(19 citation statements)

References 15 publications

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“…In this context, high recall is often deemed to be of paramount importance, although excessive numbers of false positives detract from the usability of such systems [18]. Existing initiatives designed to assist the curation of pathway and network databases include research that addresses the curation of Wnt signaling pathways [5] and an application designed to support the curation of chemical–gene–disease networks in the Comparative Toxicogenomics Database [19]. …”

Section: Introductionmentioning

confidence: 99%

A text-mining system for extracting metabolic reactions from full-text articles

et al. 2012

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BackgroundIncreasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways. However, one important category of pathway — metabolic pathways — has been largely neglected.Here we present a relatively simple method for extracting metabolic reaction information from free text that scores different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence and location of stemmed keywords. This method extends an approach that has proved effective in the context of the extraction of protein–protein interactions.ResultsWhen evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the well-known protein-protein interaction extraction task.ConclusionsWe conclude that automated metabolic pathway construction is more tractable than has often been assumed, and that (as in the case of protein–protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed.

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Section: Introductionmentioning

confidence: 99%

A text-mining system for extracting metabolic reactions from full-text articles

et al. 2012

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“…LGP has been proven to work in various biomedical text mining tasks[20–22] and has evolved into an open source project that is updated continuously. A recent update includes a better adaptation for biomedical text (BioLG).…”

Section: Methodsmentioning

confidence: 99%

Automated extraction of precise protein expression patterns in lymphoma by text mining abstracts of immunohistochemical studies

Chang

Popescu

Arthur

2013

Journal of Pathology Informatics

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Background:In general, surgical pathology reviews report protein expression by tumors in a semi-quantitative manner, that is, -, -/+, +/-, +. At the same time, the experimental pathology literature provides multiple examples of precise expression levels determined by immunohistochemical (IHC) tissue examination of populations of tumors. Natural language processing (NLP) techniques enable the automated extraction of such information through text mining. We propose establishing a database linking quantitative protein expression levels with specific tumor classifications through NLP.Materials and Methods:Our method takes advantage of typical forms of representing experimental findings in terms of percentages of protein expression manifest by the tumor population under study. Characteristically, percentages are represented straightforwardly with the % symbol or as the number of positive findings of the total population. Such text is readily recognized using regular expressions and templates permitting extraction of sentences containing these forms for further analysis using grammatical structures and rule-based algorithms.Results:Our pilot study is limited to the extraction of such information related to lymphomas. We achieved a satisfactory level of retrieval as reflected in scores of 69.91% precision and 57.25% recall with an F-score of 62.95%. In addition, we demonstrate the utility of a web-based curation tool for confirming and correcting our findings.Conclusions:The experimental pathology literature represents a rich source of pathobiological information, which has been relatively underutilized. There has been a combinatorial explosion of knowledge within the pathology domain as represented by increasing numbers of immunophenotypes and disease subclassifications. NLP techniques support practical text mining techniques for extracting this knowledge and organizing it in forms appropriate for pathology decision support systems.

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“…The methods dealing with the biomolecular information can be generally divided into three categories based on the domain of biomedical knowledge they target: (i) automated protein or gene name identification in a text Seki and Mostafa 2005;Tanabe, Xie et al 2005), (ii) literature-based functional annotation of genes and proteins (Chiang and Yu 2003;Jaeger, Gaudan et al 2008), and (iii) extracting the information on the relationships between biological molecules, such as proteins and RNAs, or genes (Hu, Narayanaswamy et al 2005;Shatkay, Hˆglund et al 2007;Lee, Yi et al 2008). The relationships detected by the third group of methods range from a co-occurrence of the genes and proteins in a text (Hoffmann and Valencia 2005) to detecting the protein-protein interactions (PPIs) (Blaschke and Valencia 2001;Marcotte, Xenarios et al 2001;Donaldson, Martin et al 2003) and identification of signal transduction networks and metabolic pathways (Friedman, Kra et al 2001;Hoffmann, Krallinger et al 2005;Santos and Eggle 2005). Being a special case of protein-protein interactions, HPIs could directly benefit from the advancements of the currently existing text mining methods.…”

Section: Current Approaches For Mining Protein-protein Interactionsmentioning

confidence: 99%