RESUMO
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 between them. RESULTS: We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. CONCLUSIONS: Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described. AVAILABILITY: The TFIDF term recognition is available as Web Service, described at http://gopubmed4.biotec.tu-dresden.de/IdavollWebService/services/CandidateTermGeneratorService?wsdl.
Assuntos
Sistemas de Gerenciamento de Base de Dados , Lipoproteínas/classificação , Lipoproteínas/metabolismo , Processamento de Linguagem Natural , Publicações Periódicas como Assunto , Terminologia como Assunto , Algoritmos , Bases de Dados Factuais , Semântica , SoftwareRESUMO
Elevated non-esterified fatty acids, triglyceride, diacylglycerol, and ceramide have all been associated with insulin resistance in muscle. We set out to investigate the role of intramyocellular lipid metabolites in the induction of insulin resistance in human primary myoblast cultures. Muscle cells were subjected to adenovirus-mediated expression of perilipin or incubated with fatty acids for 18 h, prior to insulin stimulation and measurement of lipid metabolites and rates of glycogen synthesis. Adenovirus-driven perilipin expression lead to significant accumulation of triacylglycerol in myoblasts, without any detectable effect on insulin sensitivity, as judged by the ability of insulin to stimulate glycogen synthesis. Similarly, incubation of cells with the monounsaturated fatty acid oleate resulted in triacylglycerol accumulation without inhibiting insulin action. By contrast, the saturated fatty acid palmitate induced insulin resistance. Palmitate treatment caused less accumulation of triacylglycerol than did oleate but also induced significant accumulation of both diacylglycerol and ceramide. Insulin resistance was also caused by cell-permeable analogues of ceramide, and palmitate-induced resistance was blocked in the presence of inhibitors of de novo ceramide synthesis. Oleate co-incubation completely prevented the insulin resistance induced by palmitate. Our data are consistent with ceramide being the agent responsible for insulin resistance caused by palmitate exposure. Furthermore, the triacylglycerol derived from oleate was able to exert a protective role in sequestering palmitate, thus preventing its conversion to ceramide.