Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature

By Hans-Michael Müller, Eimear Kenny, P. Sternberg. 2004

730 citations recorded by the source corpus.

We have developed Textpresso, a new text-mining system for scientific literature whose capabilities go far beyond those of a simple keyword search engine. Textpresso's two major elements are a collection of the full text of scientific articles split into individual sentences, and the implementation of categories of terms for which a database of articles and individual sentences can be searched. The categories are classes of biological concepts (e.g., gene, allele, cell or cell group, phenotype, etc.) and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e.g., biological process, etc.). Together they form a catalog of types of objects and concepts called an ontology. After this ontology is populated with terms, the whole corpus of articles and abstracts is marked up to identify terms of these categories. The current ontology comprises 33 categories of terms. A search engine enables the user to search for one or a combination of these tags and/or keywords within a sentence or document, and as the ontology allows word meaning to be queried, it is possible to formulate semantic queries. Full text access increases recall of biological data types from 45% to 95%. Extraction of particular biological facts, such as gene-gene interactions, can be accelerated significantly by ontologies, with Textpresso automatically performing nearly as well as expert curators to identify sentences; in searches for two uniquely named genes and an interaction term, the ontology confers a 3-fold increase of search efficiency. Textpresso currently focuses on Caenorhabditis elegans literature, with 3,800 full text articles and 16,000 abstracts. The lexicon of the ontology contains 14,500 entries, each of which includes all versions of a specific word or phrase, and it includes all categories of the Gene Ontology database. Textpresso is a useful curation tool, as well as search engine for researchers, and can readily be extended to other organism-specific corpora of text. Textpresso can be accessed at http://www.textpresso.org or via WormBase at http://www.wormbase.org.

Subfields: Molecular Biology

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “In addition to hospital-collected data stored in EHRs, research data are increasingly available, including public databases such as PubMed [17], Textpresso [18], Human Protein Interaction Database (HPID) [19], and MeInfoText [20].”
A survey of current work in biomedical text mining (2005, methodology): “uses a specially created ontology to flexibly combine keyword and concept co-occurrence searching of Caenorhabditis elegans (a small nematode worm) literature at the sentence level [73].”
Ontology-based information extraction: An introduction and a survey of current approaches (2010, methodology): “Such regular expressions are also used by the Textpresso system for biological literature [37], which is more of an IR system but can be seen as an OBIE system as well.”
Biomedical text mining and its applications in cancer research (2013, neutral): “Textpresso [66,67] uses an ontology, returns searching goals for classes of biological concepts (e.”
Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text (2009, methodology): “The importance of full text in information extraction has been detailed in [9].”
RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation (2007, methodology): “RegulonDB literature can now be searched with the Textpresso text-mining engine (15), customized for E.”
The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools (2011, background): “Textpresso is an information extracting and processing package for biological literature (41).”
Saccharomyces Genome Database: the genomics resource of budding yeast (2011, methodology): “org (27)] search tool is available to search approximately 50 000 papers collected by the SGD project.”
WormBase: a comprehensive resource for nematode research (2009, methodology): “In addition, we employ Textpresso for fact extraction, notably for curation of Gene Ontology (GO) cellular component terms (13).”
The BioGRID Interaction Database: 2011 update (2010, background): “Text-mining approaches have been developed that enrich for publications with speciﬁc data attributes, including Textpresso (16) and iHOP (17).”
Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support (2008, methodology): “Several biomedical search engines exploit MeSH and the UMLS to provide access to the biomedical literature, including SAPHIRE [81], Essie [82] and Textpresso [83], as well as web resources for consumers (e.g., WRAPIN [84], MedicoPort [85]).”
The Gene Ontology (GO) project in 2006 (2005, methodology): “For example, GO terms have been used in the Textpresso text mining system and in the BioCreAtIvE text mining assessment (13,27,28).”
The BioGRID interaction database: 2015 update (2014, methodology): “For example, BioGRID has several projects that are facilitated by Support Vector Machine (SVM) analyses carried out in collaboration with the Textpresso text-mining group (56).”
Text-mining and information-retrieval services for molecular biology (2005, methodology): “Textpresso [http://www.textpresso.org] co m m ent review s repo rts depo sited research interactio ns info rm atio n refereed research Genome Biology 2005, 6:224 50.”
Macronuclear Genome Sequence of the Ciliate Tetrahymena thermophila, a Model Eukaryote (2006, neutral): “genes are curated and provided, including community-approved gene names, other nonstandard aliases, nucleotide and amino acid sequences, and literature citations. In addition, free-text descriptions are associated with predicted gene models, and full-text searching is provided using Textpresso [”