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1.
Nucleic Acids Res ; 42(Database issue): D358-63, 2014 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-24234451

RESUMEN

IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple additional curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their separate efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org).


Asunto(s)
Bases de Datos de Proteínas , Mapeo de Interacción de Proteínas , Internet , Programas Informáticos
2.
Genome Res ; 13(5): 965-79, 2003 May.
Artículo en Inglés | MEDLINE | ID: mdl-12695321

RESUMEN

The aim of the present study was to generate hypotheses on the involvement of uncharacterized genes in biological processes. To this end, supervised learning was used to analyze microarray-derived time-series gene expression data. Our method was objectively evaluated on known genes using cross-validation and provided high-precision Gene Ontology biological process classifications for 211 of the 213 uncharacterized genes in the data set used. In addition, new roles in biological process were hypothesized for known genes. Our method uses biological knowledge expressed by Gene Ontology and generates a rule model associating this knowledge with minimal characteristic features of temporal gene expression profiles. This model allows learning and classification of multiple biological process roles for each gene and can predict participation of genes in a biological process even though the genes of this class exhibit a wide variety of gene expression profiles including inverse coregulation. A considerable number of the hypothesized new roles for known genes were confirmed by literature search. In addition, many biological process roles hypothesized for uncharacterized genes were found to agree with assumptions based on homology information. To our knowledge, a gene classifier of similar scope and functionality has not been reported earlier.


Asunto(s)
Perfilación de la Expresión Génica/métodos , Regulación de la Expresión Génica/fisiología , Terminología como Asunto , Animales , Genes/fisiología , Genes Bacterianos/fisiología , Genes de Insecto/fisiología , Genes Relacionados con las Neoplasias/fisiología , Humanos , Ratones , Modelos Genéticos , Valor Predictivo de las Pruebas , Ratas , Sensibilidad y Especificidad , Homología de Secuencia de Ácido Nucleico , Tiempo
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