1. 2641-2664 of 2861 « 1 2 ... 108 109 110 111 112 113 114 ... 118 119 120 »
    1. Normalizing biomedical terms by minimizing ambiguity and variability.

      Related Articles Normalizing biomedical terms by minimizing ambiguity and variability. BMC Bioinformatics. 2008;9 Suppl 3:S2 Authors: Tsuruoka Y, McNaught J, Ananiadou S BACKGROUND: One of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms. Soft string matching is a possible solution to the problem, but its inherent heavy computational cost discourages its use when the dictionaries are large or when real time processing is required. A less computationally demanding approach is to normalize the terms by using heuristic rules, which enables ...
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    2. Exploiting and integrating rich features for biological literature classification.

      Related Articles Exploiting and integrating rich features for biological literature classification. BMC Bioinformatics. 2008;9 Suppl 3:S4 Authors: Wang H, Huang M, Ding S, Zhu X BACKGROUND: Efficient features play an important role in automated text classification, which definitely facilitates the access of large-scale data. In the bioscience field, biological structures and terminologies are described by a large number of features; domain dependent features would significantly improve the classification performance. How to effectively select and integrate different types of features to improve the biological literature classification performance is the major issue studied in this paper. RESULTS: To efficiently classify ...
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    3. Assessment of disease named entity recognition on a corpus of annotated sentences.

      Related Articles Assessment of disease named entity recognition on a corpus of annotated sentences. BMC Bioinformatics. 2008;9 Suppl 3:S3 Authors: Jimeno A, Jimenez-Ruiz E, Lee V, Gaudan S, Berlanga R, Rebholz-Schuhmann D BACKGROUND: In recent years, the recognition of semantic types from the biomedical scientific literature has been focused on named entities like protein and gene names (PGNs) and gene ontology terms (GO terms). Other semantic types like diseases have not received the same level of attention. Different solutions have been proposed to identify disease named entities in the scientific literature. While matching the terminology with language patterns ...
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    4. New challenges for text mining: mapping between text and manually curated pathways.

      Related Articles New challenges for text mining: mapping between text and manually curated pathways. BMC Bioinformatics. 2008;9 Suppl 3:S5 Authors: Oda K, Kim JD, Ohta T, Okanohara D, Matsuzaki T, Tateisi Y, Tsujii J BACKGROUND: Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. RESULTS ...
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    5. Structuring an event ontology for disease outbreak detection.

      Related Articles Structuring an event ontology for disease outbreak detection. BMC Bioinformatics. 2008;9 Suppl 3:S8 Authors: Kawazoe A, Chanlekha H, Shigematsu M, Collier N BACKGROUND: This paper describes the design of an event ontology being developed for application in the machine understanding of infectious disease-related events reported in natural language text. This event ontology is designed to support timely detection of disease outbreaks and rapid judgment of their alerting status by 1) bridging a gap between layman's language used in disease outbreak reports and public health experts' deep knowledge, and 2) making multi-lingual information available. CONSTRUCTION AND ...
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    6. Monitoring the evolutionary aspect of the Gene Ontology to enhance predictability and usability.

      Related Articles Monitoring the evolutionary aspect of the Gene Ontology to enhance predictability and usability. BMC Bioinformatics. 2008;9 Suppl 3:S7 Authors: Park JC, Kim TE, Park J BACKGROUND: Much effort is currently made to develop the Gene Ontology (GO). Due to the dynamic nature of information it addresses, GO undergoes constant updates whose results are released at regular intervals as separate versions. Although there are a large number of computational tools to aid the development of GO, they are operating on a particular version of GO, making it difficult for GO curators to anticipate the full impact of ...
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    7. Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction.

      Related Articles Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction. BMC Bioinformatics. 2008;9 Suppl 3:S9 Authors: Gobeill J, Tbahriti I, Ehrler F, Mottaz A, Veuthey AL, Ruch P BACKGROUND: This paper describes and evaluates a sentence selection engine that extracts a GeneRiF (Gene Reference into Functions) as defined in ENTREZ-Gene based on a MEDLINE record. Inputs for this task include both a gene and a pointer to a MEDLINE reference. In the suggested approach we merge two independent sentence extraction strategies. The first proposed strategy (LASt) uses argumentative features, inspired by discourse-analysis models. The second ...
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    8. Terminologies for text-mining; an experiment in the lipoprotein metabolism domain.

      Related Articles Terminologies for text-mining; an experiment in the lipoprotein metabolism domain. BMC Bioinformatics. 2008;9 Suppl 4:S2 Authors: Alexopoulou D, Wächter T, Pickersgill L, Eyre C, Schroeder M BACKGROUND: The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations ...
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    9. Ontology-guided data preparation for discovering genotype-phenotype relationships.

      Related Articles Ontology-guided data preparation for discovering genotype-phenotype relationships. BMC Bioinformatics. 2008;9 Suppl 4:S3 Authors: Coulet A, Smaïl-Tabbone M, Benlian P, Napoli A, Devignes MD BACKGROUND: Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning. RESULTS: This paper presents a ...
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    10. Ontology-based, Tissue MicroArray oriented, image centered tissue bank.

      Related Articles Ontology-based, Tissue MicroArray oriented, image centered tissue bank. BMC Bioinformatics. 2008;9 Suppl 4:S4 Authors: Viti F, Merelli I, Caprera A, Lazzari B, Stella A, Milanesi L BACKGROUND: Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack ...
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    11. RDFScape: Semantic Web meets systems biology.

      Related Articles RDFScape: Semantic Web meets systems biology. BMC Bioinformatics. 2008;9 Suppl 4:S6 Authors: Splendiani A BACKGROUND: The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is ...
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    12. A Semantic Web for bioinformatics: goals, tools, systems, applications. Proceedings of the Seventh Nettab (Network Tools and Applications in Biology) Workshop. June 12-15, 2007. Pisa, Italy.

      A Semantic Web for bioinformatics: goals, tools, systems, applications. Proceedings of the Seventh NETTAB (Network Tools and Applications in Biology) Workshop. June 12-15, 2007. Pisa, Italy. BMC Bioinformatics. 2008;9 Suppl 4:S1-13 Authors: PMID: 18536085 [PubMed - indexed for MEDLINE]
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    13. An fMRI-based structural equation model for natural language processing shows age-dependent changes in brain connectivity.

      Related Articles An fMRI-based structural equation model for natural language processing shows age-dependent changes in brain connectivity. J Acoust Soc Am. 2008 May;123(5):3425 Authors: Holland SK, Karunanayaka P, Plante EJ, Schmithorst VJ Structural Equation Modeling (SEM) or path analysis is a multivariate analytic tool that is used to test hypothesis about causal influences among measured or latent variables. When applied to functional neuroimaging (fMRI) data, SEM combines interregional covariance and neuroanatomy to investigate brain connectivity and the dynamic flow of information across neural networks. We have investigate Linear SEM or LSEM as a first step in estimating ...
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    14. Managing knowledge in neuroscience.

      Related Articles Managing knowledge in neuroscience. Methods Mol Biol. 2007;401:3-21 Authors: Crasto CJ, Shepherd GM Processing text from scientific literature has become a necessity due to the burgeoning amounts of information that are fast becoming available, stemming from advances in electronic information technology. We created a program, NeuroText ( http://senselab.med.yale.edu/textmine/neurotext.pl ), designed specifically to extract information relevant to neuroscience-specific databases, NeuronDB and CellPropDB ( http://senselab.med.yale.edu/senselab/ ), housed at the Yale University School of Medicine. NeuroText extracts relevant information from the Neuroscience literature in a two-step process: each step parses text ...
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    15. Creating neuroscience ontologies.

      Related Articles Creating neuroscience ontologies. Methods Mol Biol. 2007;401:67-87 Authors: Bowden DM, Dubach M, Park J The insufficiency of terminological standards in neuroscience is increasingly recognized as a serious obstacle to interoperability. Adoption of a controlled vocabulary is a successful solution for small numbers of groups that work closely together but is impractical for large numbers of groups who represent diverse areas of research, index information by various legitimate nomenclatures, or publish in different languages. Interoperability among such disparate databases requires a translation mechanism, or "mediator," to enable communication and data sharing among databases. Shared ontologies are essential ...
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    16. XML for data representation and model specification in neuroscience.

      Related Articles XML for data representation and model specification in neuroscience. Methods Mol Biol. 2007;401:53-66 Authors: Crook SM, Howell FW EXtensible Markup Language (XML) technology provides an ideal representation for the complex structure of models and neuroscience data, as it is an open file format and provides a language-independent method for storing arbitrarily complex structured information. XML is composed of text and tags that explicitly describe the structure and semantics of the content of the document. In this chapter, we describe some of the common uses of XML in neuroscience, with case studies in representing neuroscience data and ...
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    17. PIE: an online prediction system for protein-protein interactions from text.

      Related Articles PIE: an online prediction system for protein-protein interactions from text. Nucleic Acids Res. 2008 May 28; Authors: Kim S, Shin SY, Lee IH, Kim SJ, Sriram R, Zhang BT Protein-protein interaction (PPI) extraction has been an important research topic in bio-text mining area, since the PPI information is critical for understanding biological processes. However, there are very few open systems available on the Web and most of the systems focus on keyword searching based on predefined PPIs. PIE (Protein Interaction information Extraction system) is a configurable Web service to extract PPIs from literature, including user-provided papers as well ...
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    18. Synonym set extraction from the biomedical literature by lexical pattern discovery.

      Related Articles Synonym set extraction from the biomedical literature by lexical pattern discovery. BMC Bioinformatics. 2008;9:159 Authors: McCrae J, Collier N BACKGROUND: Although there are a large number of thesauri for the biomedical domain many of them lack coverage in terms and their variant forms. Automatic thesaurus construction based on patterns was first suggested by Hearst 1, but it is still not clear how to automatically construct such patterns for different semantic relations and domains. In particular it is not certain which patterns are useful for capturing synonymy. The assumption of extant resources such as parsers is also ...
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    19. Discovering gene annotations in biomedical text databases.

      Related Articles Discovering gene annotations in biomedical text databases. BMC Bioinformatics. 2008;9:143 Authors: Cakmak A, Ozsoyoglu G BACKGROUND: Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. RESULTS: In this article, we present an automated genomic entity annotation system ...
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    20. Improving protein function prediction methods with integrated literature data.

      Related Articles Improving protein function prediction methods with integrated literature data. BMC Bioinformatics. 2008;9:198 Authors: Gabow AP, Leach SM, Baumgartner WA, Hunter LE, Goldberg DS BACKGROUND: Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically ...
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    21. Searching the NCBI databases using Entrez.

      Related Articles Searching the NCBI databases using Entrez. Curr Protoc Bioinformatics. 2006 Mar;Chapter 1:Unit 1.3 Authors: Baxevanis AD One of the most widely-used interfaces for the retrieval of information from biological databases is the NCBI Entrez system. Entrez capitalizes on the fact that there are pre-existing, logical relationships between the individual entries found in numerous public databases. The existence of such natural connections, mostly biological in nature, argued for the development of a method through which all the information about a particular biological entity could be found without having to sequentially visit and query disparate databases. Two ...
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    22. Enhancing knowledge representations by ontological relations.

      Enhancing knowledge representations by ontological relations. Stud Health Technol Inform. 2008;136:791-6 Authors: Denecke K Several medical natural language processing (NLP) systems currently base on ontologies that provide the domain knowledge. But, relationships between concepts defined in ontologies as well as relations predefined in a semantic network are widely unused in this context. The objective of this paper is to analyse potentials of using ontological relations to produce correct semantic structures for a medical document automatically and to ameliorate and enrich these structures. Knowledge representations to unstructured medical narratives are generated by means of the method SeReMeD. This approach ...
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    2641-2664 of 2861 « 1 2 ... 108 109 110 111 112 113 114 ... 118 119 120 »
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