PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine

Donaldson, Ian; Martin, Joel; Bruijn, Berry de; Wolting, Cheryl; Lay, Vicki; Tuekam, Brigitte; Zhang, Shudong; Baskin, Berivan; Bader, Gary D.; Michalickova, Katerina; Pawson, Tony; Hogue, Christopher W. V.

doi:10.1186/1471-2105-4-11

Cited by 254 publications

(42 citation statements)

References 28 publications

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“…We anticipate that the two most useful formats will be the Cytoscape SIF (as noted below) and CSV (for Microsoft Excel), from the perspective of the research biologist user of BIND. PreBIND (16) is currently a separate information system with interactions derived from text mining of MEDLINE abstracts (17). When the user cannot find BIND records, we suggest that they next search through the PreBIND database.…”

Section: Database Query and Retrievalmentioning

confidence: 99%

The Biomolecular Interaction Network Database and related tools 2005 update

Alfarano¹,

Andrade²,

Anthony³

et al. 2004

Nucleic Acids Research

514

378

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The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machinereadable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues. INTRODUCTIONIn light of the vast scientific resources made available through genomics, the science of deciphering molecular mechanisms is expanding rapidly. Scientists who once hunted for disease genes or sought to distinguish key concepts in evolution are now turning their attention to the details of molecular assembly and mechanism to further understand medicine and the key concepts underlying biology. The Biomolecular Interaction Network Database (BIND) was designed to store complete information about molecular assembly through a database structure in order to archive interactions and reactions arising from biopolymers (protein, RNA and DNA), as well as small molecules, lipids and carbohydrates. Detailed information about molecular mechanism, such as the chemical product(s) of an enzymatic reaction, can be encoded in BIND. The underlying ontology of the BIND database is chemistry, and as such, BIND is capable of storing information about molecular interactions to atomic resolution. The taxonomic scope of BIND is

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Section: Database Query and Retrievalmentioning

confidence: 99%

The Biomolecular Interaction Network Database and related tools 2005 update

Alfarano¹,

Andrade²,

Anthony³

et al. 2004

Nucleic Acids Research

514

378

View full text Add to dashboard Cite

show abstract

“…They have high precision but low recall, because recognition patterns are usually too specific. Other machine learning approaches have classified abstracts and sentences for relevant interactions, but have not extracted information (Marcotte et al 2001; Donaldson et al 2003). For a more detailed report of these and related projects, see reviews by Andrade and Bork (2000), de Bruijn and Martin (2002), and Staab (2002).…”

Section: Introductionmentioning

confidence: 99%

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

2004

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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.

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“…PreBind (Donaldson et al 2003) is an approach complementary to BIND for finding interaction data in the over 14 million PubMed abstracts. PreBind functions as follows: (1) SVM technology is used to identify articles about biomolecular interactions and confirm sentences that mention specific protein-protein interactions; (2) protein names and their gene symbols are derived from a non-redundant sequence database (RefSeq) and from the S. cerevisiae Genome Database (SGD); (3) this information extraction system is coupled to a human-reviewed data-entry queue for a publicly available BIND.…”

Section: Literature Mining For Protein-protein Interactionsmentioning

confidence: 99%

Experimental and bioinformatic approaches for interrogating protein–protein interactions to determine protein function

Droit¹,

Poirier

Hunter³

2005

Journal of Molecular Endocrinology

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An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. One strategy to determine protein function is to identify the protein-protein interactions. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore this disparate data and to uncover biologically relevant interactions and pathways. In parallel, there is clearly a need for the development of approaches that can predict novel protein-protein interaction networks in silico. Here, we present an overview of different experimental and bioinformatic methods to elucidate protein-protein interactions.

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PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine

Cited by 254 publications

References 28 publications

The Biomolecular Interaction Network Database and related tools 2005 update

The Biomolecular Interaction Network Database and related tools 2005 update

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

Experimental and bioinformatic approaches for interrogating protein–protein interactions to determine protein function

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