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Corpus based learning of stochastic, context-free grammars combined with Hidden Markov Models for tRNA modelling

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Corpus based learning of stochastic, context-free grammars combined with Hidden Markov Models for tRNA modelling

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dc.contributor.author García Gómez, Juan Miguel es_ES
dc.contributor.author Benedí Ruiz, José Miguel es_ES
dc.contributor.author Vicente Robledo, Javier es_ES
dc.contributor.author Robles Viejo, Montserrat es_ES
dc.date.accessioned 2014-12-04T07:35:59Z
dc.date.available 2014-12-04T07:35:59Z
dc.date.issued 2005
dc.identifier.issn 1744-5485
dc.identifier.uri http://hdl.handle.net/10251/45150
dc.description.abstract [EN] In this paper, a new method for modelling tRNA secondary structures is presented. This method is based on the combination of stochastic context-free grammars (SCFG) and Hidden Markov Models (HMM). HMM are used to capture the local relations in the loops of the molecule (nonstructured regions) and SCFG are used to capture the long term relations between nucleotides of the arms (structured regions). Given annotated public databases, the HMM and SCFG models are learned by means of automatic inductive learning methods. Two SCFG learning methods have been explored. Both of them take advantage of the structural information associated with the training sequences: one of them is based on a stochastic version of the Sakakibara algorithm and the other one is based on a Corpus based algorithm. A final model is then obtained by merging of the HMM of the nonstructured regions and the SCFG of the structured regions. Finally, the performed experiments on the tRNA sequence corpus and the non-tRNA sequence corpus give significant results. Comparative experiments with another published method are also presented. es_ES
dc.description.sponsorship We would like to thank Diego Linares and Joan Andreu Sanchez for answering all our questions about SCFG, as well as Satoshi Sekine for his evaluation software. We would also like to thank the Ministerio de Sanidad y Consumo of Spain for the grants to the INBIOMED consortium.
dc.language Inglés es_ES
dc.publisher Inderscience es_ES
dc.relation.ispartof International Journal of Bioinformatics Research and Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Hidden Markov Models (HMM) es_ES
dc.subject RNA es_ES
dc.subject Secondary structure modelling es_ES
dc.subject Language modelling es_ES
dc.subject Grammatical inference es_ES
dc.subject Stochastic context-free grammar (SCFG) es_ES
dc.subject Syntactic pattern recognition es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Corpus based learning of stochastic, context-free grammars combined with Hidden Markov Models for tRNA modelling es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1504/IJBRA.2005.007908
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació es_ES
dc.description.bibliographicCitation García Gómez, JM.; Benedí Ruiz, JM.; Vicente Robledo, J.; Robles Viejo, M. (2005). Corpus based learning of stochastic, context-free grammars combined with Hidden Markov Models for tRNA modelling. International Journal of Bioinformatics Research and Applications. 1(3):305-318. doi:10.1504/IJBRA.2005.007908 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1504/IJBRA.2005.007908 es_ES
dc.description.upvformatpinicio 305 es_ES
dc.description.upvformatpfin 318 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 1 es_ES
dc.description.issue 3 es_ES
dc.relation.senia 28800
dc.contributor.funder Ministerio de Sanidad y Consumo


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