@article {Friedman2006BioOntology,
	title = {Bio-Ontology and text: bridging the modeling gap},
	journal = {Bioinformatics},
	volume = {22},
	number = {19},
	year = {2006},
	month = {October},
	pages = {2421{\textendash}2429},
	abstract = {Motivation: Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text. Results: A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90\% of the information could be represented (95\% confidence interval: 86-93\%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes). Availability: http://zellig.cpmc.columbia.edu/PGschema Contact: Friedman@dbmi.columbia.edu or Lussier@uchicago.edu 10.1093/bioinformatics/btl405},
	keywords = {data-mining, it, ontologies, text-mining},
	doi = {10.1093/bioinformatics/btl405},
	url = {http://dx.doi.org/10.1093/bioinformatics/btl405},
	author = {Friedman, Carol and Borlawsky, Tara and Shagina, Lyudmila and Xing, H. Rosie and Lussier, Yves A.}
}
@article {delaCalle2009BIRI,
	title = {BIRI: a new approach for automatically discovering and indexing available public bioinformatics resources from the literature.},
	journal = {BMC bioinformatics},
	volume = {10},
	number = {1},
	year = {2009},
	month = {October},
	pages = {320+},
	abstract = {BACKGROUND: The rapid evolution of Internet technologies and the collaborative approaches that dominate the field have stimulated the development of numerous bioinformatics resources. To address this new framework, several initiatives have tried to organize these services and resources. In this paper, we present the BioInformatics Resource Inventory (BIRI), a new approach for automatically discovering and indexing available public bioinformatics resources using information extracted from the scientific literature. The index generated can be automatically updated by adding additional manuscripts describing new resources. We have developed web services and applications to test and validate our approach. It has not been designed to replace current indexes but to extend their capabilities with richer functionalities. RESULTS: We developed a web service to provide a set of high-level query primitives to access the index. The web service can be used by third-party web services or web-based applications. To test the web service, we created a pilot web application to access a preliminary knowledge base of resources. We tested our tool using an initial set of 400 abstracts. Almost 90\% of the resources described in the abstracts were correctly classified. More than 500 descriptions of functionalities were extracted. CONCLUSION: These experiments suggest the feasibility of our approach for automatically discovering and indexing current and future bioinformatics resources. Given the domain-independent characteristics of this tool, it is currently being applied by the authors in other areas, such as medical nanoinformatics. BIRI is available at http://edelman.dia.fi.upm.es/biri/.},
	keywords = {bioinformatics, data-mining, it, text-mining},
	issn = {1471-2105},
	doi = {10.1186/1471-2105-10-320},
	url = {http://dx.doi.org/10.1186/1471-2105-10-320},
	author = {de la Calle, Guillermo and Garc\'{\i}a-Remesal, Miguel and Chiesa, Stefano and de la Iglesia, Diana and Maojo, Victor}
}
