Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/43411
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dc.contributor.author Autor Simón, Diego-
dc.contributor.author Autor Borsani, Omar-
dc.contributor.author Autor Filippi, Carla V.-
dc.contributor.filiacion Filiación Simón Diego, Universidad de la República (Uruguay). Facultad de Ciencias. Centro de Investigaciones Nucleares.-
dc.contributor.filiacion Filiación Borsani Omar, Universidad de la República (Uruguay). Facultad de Agronomía.-
dc.contributor.filiacion Filiación Filippi Carla V., Universidad de la República (Uruguay). Facultad de Agronomía-
dc.date.accessioned Fecha ingreso 2024-04-11T12:33:24Z-
dc.date.available Fecha disponible 2024-04-11T12:33:24Z-
dc.date.issued Año de publicación 2022-
dc.description.abstract Resumen Background: Plant innate immunity relies on a broad repertoire of receptor proteins that can detect pathogens and trigger an effective defense response. Bioinformatic tools based on conserved domain and sequence similarity are within the most popular strategies for protein identification and characterization. However, the multi-domain nature, high sequence diversity and complex evolutionary history of disease resistance (DR) proteins make their prediction a real challenge. Here we present RFPDR, which pioneers the application of Random Forest (RF) for Plant DR protein prediction. Methods: A recently published collection of experimentally validated DR proteins was used as a positive dataset, while 10x10 nested datasets, ranging from 400-4,000 non-DR proteins, were used as negative datasets. A total of 9,631 features were extracted from each protein sequence, and included in a full dimension (FD) RFPDR model. Sequence selection was performed, to generate a reduced-dimension (RD) RFPDR model. Model performances were evaluated using an 80/20 (training/testing) partition, with 10- cross fold validation, and compared to baseline, sequence-based and state-of-the-art strategies. To gain some insights into the underlying biology, the most discriminatory sequence-based features in the RF classifier were identified. Results and Discussion: RD-RFPDR showed to be sensitive (86.4 ± 4.0%) and specific (96.9 ± 1.5%) for identifying DR proteins, while robust to data imbalance. Its high performance and robustness, added to the fact that RD-RFPDR provides valuable information related to DR proteins underlying properties, make RD-RFPDR an interesting approach for DR protein prediction, complementing the state-of-the-art strategies.es
dc.format.extent Extensión 20 h.es
dc.format.mimetype Formato application/pdfes
dc.identifier.citation Citación Simón, D, Borsani, O y Filippi, C. "RFPDR: a random forest approach for plant disease resistance protein prediction". PeerJ. [en línea] 2022, 10: e11683. 20 h. DOI: 10.7717/peerj.11683.es
dc.identifier.doi DOI 10.7717/peerj.11683-
dc.identifier.issn ISSN 2167-8359-
dc.identifier.uri URI https://hdl.handle.net/20.500.12008/43411-
dc.language.iso Idioma enes
dc.publisher Editorial PeerJes
dc.relation.ispartof EN PeerJ, 2022, 10: e11683.es
dc.rights Derechos Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)es
dc.rights.licence Licencia Licencia Creative Commons Atribución (CC - By 4.0)es
dc.subject Palabras clave Disease resistancees
dc.subject Palabras clave Plant immunityes
dc.subject Palabras clave Defense responsees
dc.subject Palabras clave Machine learninges
dc.subject Palabras clave Random forestes
dc.title Título RFPDR: a random forest approach for plant disease resistance protein predictiones
dc.type Tipo de documento Artículoes

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