Unsupervised learning analysis on the proteomes of Zika virus

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:29:40Z
Fecha de publicación2024-01-01
ResumenBackground The Zika virus (ZIKV), which is transmitted by mosquito vectors to nonhuman primates and humans, causes devastating outbreaks in the poorest tropical regions of the world. Molecular epidemiology, supported by clustering phylogenetic gold standard studies using sequence data, has provided valuable information for tracking and controlling the spread of ZIKV. Unsupervised learning (UL), a form of machine learning algorithm, can be applied on the datasets without the need of known information for training. Methods In this work, unsupervised Random Forest (URF), followed by the application of dimensional reduction algorithms such as principal component analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), t-distributed stochastic neighbor embedding (t-SNE), and autoencoders were used to uncover hidden patterns from polymorphic amino acid sites extracted on the proteome ZIKV multi-alignments, without the need of an underlying evolutionary model. Results The four UL algorithms revealed specific host and geographical clustering patterns for ZIKV. Among the four dimensionality reduction (DR) algorithms, the performance was better for UMAP. The four algorithms allowed the identification of imported viruses for specific geographical clusters. The UL dimension coordinates showed a significant correlation with phylogenetic tree branch lengths and significant phylogenetic dependence in Abouheif’s Cmean and Pagel’s Lambda tests (p value < 0.01) that showed comparable performance with the phylogenetic method. This analytical strategy was generalizable to an external large dengue type 2 dataset. Conclusion These UL algorithms could be practical evolutionary analytical techniques to track the dispersal of viral pathogens.es_ES
Doihttps://doi.org/10.7717/peerj-cs.2443es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/1639
Idiomaeses_ES
EditorialPeerJes_ES
RelaciónPeerJ Computer Sciencees_ES
URL relacionadohttps://doi.org/10.7717/peerj-cs.2443es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuentePeerJ Computer Science
TítuloUnsupervised learning analysis on the proteomes of Zika viruses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorLara-Ramírez, Edgar E.
AutorRivera, Gildardo
AutorOliva-Hernández, Amanda Alejandra
AutorBocanegra-Garcia, Virgilio
AutorLópez, Jesús Adrián
AutorGuo, Xianwu
AutorLara-Ramírez, Edgar E.es_ES
AutorRivera, Gildardoes_ES
AutorOliva-Hernández, Amanda Alejandraes_ES
AutorBocanegra-Garcia, Virgilioes_ES
AutorLópez, Jesús Adriánes_ES
AutorGuo, Xianwues_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Rango de páginase2443es_ES
URL relacionadahttps://doi.org/10.7717/peerj-cs.2443
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen10es_ES

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