Mostrar el registro sencillo del ítem
dc.contributor.author
Pazos Obregón, Flavio
dc.contributor.author
Palazzo, Martin
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.contributor.author
Soto, Pablo
dc.contributor.author
Guerberoff, Gustavo
dc.contributor.author
Yankilevich, Patricio
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.contributor.author
Cantera, Rafael
dc.date.available
2021-02-03T13:20:53Z
dc.date.issued
2019-12
dc.identifier.citation
Pazos Obregón, Flavio; Palazzo, Martin; Soto, Pablo; Guerberoff, Gustavo; Yankilevich, Patricio; et al.; An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach; BioMed Central; BMC Genomics; 20; 1; 12-2019; 1-8
dc.identifier.issn
1471-2164
dc.identifier.uri
http://hdl.handle.net/11336/124570
dc.description.abstract
Background: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes which we postulated to be very enriched in genes with a still undocumented synaptic function. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we use these new empirical data to evaluate our original prediction. We also implement a series of changes to the training scheme of our model and using the new data we demonstrate that this improves its predictive power. Finally, we added the new synaptic genes to the training set and trained a new model, obtaining a new, enhanced catalogue of putative synaptic genes. Results: The retrospective analysis demonstrate that our original catalogue was significantly enriched in new synaptic genes. When the changes to the training scheme were implemented using the original training set we obtained even higher enrichment. Finally, applying the new training scheme with a training set including the 79 new synaptic genes, resulted in an enhanced catalogue of putative synaptic genes. Here we present this new catalogue and announce that a regularly updated version will be available online at: Http://synapticgenes.bnd.edu.uy Conclusions: We show that training an ensemble of machine learning classifiers solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data provided by the scientific community, we validated our original approach, improved our model an obtained an arguably more precise prediction. This approach reduces the number of genes to be tested through hypothesis-driven experimentation and will facilitate our understanding of neuronal function. Availability: Http://synapticgenes.bnd.edu.uy
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
BioMed Central
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DROSOPHILA MELANOGASTER
dc.subject
GENE FUNCTION PREDICTION
dc.subject
MACHINE LEARNING
dc.subject
SYNAPTIC GENES
dc.subject
TEMPORAL TRANSCRIPTION PROFILES
dc.subject.classification
Genética y Herencia
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.subject.classification
Ciencias Biológicas
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.title
An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-11-20T19:54:36Z
dc.journal.volume
20
dc.journal.number
1
dc.journal.pagination
1-8
dc.journal.pais
Reino Unido
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.journal.ciudad
Londres
dc.description.fil
Fil: Pazos Obregón, Flavio. Instituto de Investigaciones Biológicas "Clemente Estable"; Uruguay
dc.description.fil
Fil: Palazzo, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina
dc.description.fil
Fil: Soto, Pablo. Instituto de Investigaciones Biológicas "Clemente Estable"; Uruguay
dc.description.fil
Fil: Guerberoff, Gustavo. Universidad de la República; Uruguay
dc.description.fil
Fil: Yankilevich, Patricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina
dc.description.fil
Fil: Cantera, Rafael. Instituto de Investigaciones Biológicas "Clemente Estable"; Uruguay
dc.journal.title
BMC Genomics
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1186/s12864-019-6380-z
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6380-z
Archivos asociados