Using the biological taxonomy to access biological literature with PathBinderH

Ding, Jing; Viswanathan, Kartik; Berleant, Daniel; Hughes, LaRon Maurice; Wurtele, Eve Syrkin; Ashlock, Dan; Dickerson, Julie A.; Fulmer, Andrew W.; Schnable, Patrick S.

doi:10.1093/bioinformatics/bti381

Cited by 19 publications

(14 citation statements)

References 7 publications

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“…(We note that the main factor influencing processing time is the Java XML document parsing rather than the actual species name tagging.) These species tagging experiments far exceed the scale of any previous report [7,10,14,23,25,36,37,41], and represent one of the first applications of text mining to the entire PMC OA corpus (see also [15,54,55]). Over 30 million species tags for over 57,000 different species were detected in MEDLINE, and over 4 million species tags for nearly 19,000 species in PMC OA.…”

Section: Resultsmentioning

confidence: 92%

“…While a number of tools performing species name recognition and/or normalization of both scientific names and synonyms have been reported previously [7,10,14,23,25,33,36,37,41,61], the work presented here contributes to the field in a number of unique ways. These include availability of a robust, open-source, stand-alone application (other tools are either not publically available, only available as web services or not capable of recognizing common names), scale of species tagging (all of MEDLINE and PMC OA until 2008), depth and rigour of evaluation (other tools do not perform evaluation against normalized database identifiers, or are limited to a small sample of documents) and accuracy (compared to other available tools, LINNAEUS shows better performance, mainly due to better handling of ambiguous mentions and inclusion of additional synonyms).…”

Section: Discussionmentioning

confidence: 99%

“…Document search queries could be filtered on the basis of which species are mentioned in the documents [14], providing researchers more fine-grained control over literature search results. This use case provides a powerful extension to simple keyword-based PubMed searches, since all synonyms of a species would be normalized to a standard database identifier, and could therefore be retrieved by any synonym used as input.…”

Section: Introductionmentioning

confidence: 99%

“…Recognized species names are typically normalized against the NCBI taxonomy [31]. For example, PathBinderH [14] is a dictionary-based web service where users can submit PubMed queries and filter the documents retrieved by species mentioned in the documents. Unfortunately, the service is currently limited to 20,000 species and is restricted to a fixed set of 65,000 of documents in MEDLINE.…”

Section: Introductionmentioning

confidence: 99%

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LINNAEUS: A species name identification system for biomedical literature

2010

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BackgroundThe task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles.ResultsIn this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions. LINNAEUS uses a dictionary-based approach (implemented as an efficient deterministic finite-state automaton) to identify species names and a set of heuristics to resolve ambiguous mentions. When compared against our manually annotated corpus, LINNAEUS performs with 94% recall and 97% precision at the mention level, and 98% recall and 90% precision at the document level. Our system successfully solves the problem of disambiguating uncertain species mentions, with 97% of all mentions in PubMed Central full-text documents resolved to unambiguous NCBI taxonomy identifiers.ConclusionsLINNAEUS is an open source, stand-alone software system capable of recognizing and normalizing species name mentions with speed and accuracy, and can therefore be integrated into a range of bioinformatics and text-mining applications. The software and manually annotated corpus can be downloaded freely at http://linnaeus.sourceforge.net/.

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Section: Resultsmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LINNAEUS: A species name identification system for biomedical literature

2010

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“…Others references are input by curators. Our internal curator tool integrates with PathBinder [5,7] to allow searching PubMed and adding selected biological references.…”

Section: Methodsmentioning

confidence: 99%

MetNet Online: a novel integrated resource for plant systems biology

Sucaet

Wang

et al. 2012

BMC Bioinformatics

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BackgroundPlants are important as foods, pharmaceuticals, biorenewable chemicals, fuel resources, bioremediation tools and general tools for recombinant technology. The study of plant biological pathways is advanced by easy access to integrated data sources. Today, various plant data sources are scattered throughout the web, making it increasingly complicated to build comprehensive datasets.ResultsMetNet Online is a web-based portal that provides access to a regulatory and metabolic plant pathway database. The database and portal integrate Arabidopsis, soybean (Glycine max) and grapevine (Vitis vinifera) data. Pathways are enriched with known or predicted information on sub cellular location. MetNet Online enables pathways, interactions and entities to be browsed or searched by multiple categories such as sub cellular compartment, pathway ontology, and GO term. In addition to this, the “My MetNet” feature allows registered users to bookmark content and track, import and export customized lists of entities. Users can also construct custom networks using existing pathways and/or interactions as building blocks.ConclusionThe site can be reached at http://www.metnetonline.org. Extensive video tutorials on how to use the site are available through http://www.metnetonline.org/tutorial/.

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Analysis of Biological Processes and Diseases Using Text Mining Approaches

Krallinger

Leitner

Valencia

2009

Methods in Molecular Biology

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A number of biomedical text mining systems have been developed to extract biologically relevant information directly from the literature, complementing bioinformatics methods in the analysis of experimentally generated data. We provide a short overview of the general characteristics of natural language data, existing biomedical literature databases, and lexical resources relevant in the context of biomedical text mining. A selected number of practically useful systems are introduced together with the type of user queries supported and the results they generate. The extraction of biological relationships, such as protein-protein interactions as well as metabolic and signaling pathways using information extraction systems, will be discussed through example cases of cancer-relevant proteins. Basic strategies for detecting associations of genes to diseases together with literature mining of mutations, SNPs, and epigenetic information (methylation) are described. We provide an overview of disease-centric and gene-centric literature mining methods for linking genes to phenotypic and genotypic aspects. Moreover, we discuss recent efforts for finding biomarkers through text mining and for gene list analysis and prioritization. Some relevant issues for implementing a customized biomedical text mining system will be pointed out. To demonstrate the usefulness of literature mining for the molecular oncology domain, we implemented two cancer-related applications. The first tool consists of a literature mining system for retrieving human mutations together with supporting articles. Specific gene mutations are linked to a set of predefined cancer types. The second application consists of a text categorization system supporting breast cancer-specific literature search and document-based breast cancer gene ranking. Future trends in text mining emphasize the importance of community efforts such as the BioCreative challenge for the development and integration of multiple systems into a common platform provided by the BioCreative Metaserver.

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Using the biological taxonomy to access biological literature with PathBinderH

Abstract: www.plantgenomics.iastate.edu/PathBinderH. Source code and databases on request.

Cited by 19 publications

References 7 publications

LINNAEUS: A species name identification system for biomedical literature

LINNAEUS: A species name identification system for biomedical literature

MetNet Online: a novel integrated resource for plant systems biology

Analysis of Biological Processes and Diseases Using Text Mining Approaches

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