Gene selection and disease prediction from gene expression data using a two-stage hetero-associative memory
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Cleofás Sánchez, Laura; Sánchez Garreta, Josep Salvador; García, Vicente
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Gene selection and disease prediction from gene expression data using a two-stage hetero-associative memoryFecha de publicación
2019Editor
Springer VerlagISSN
2192-6360Cita bibliográfica
CLEOFAS-SÁNCHEZ, Laura; SÁNCHEZ, J. Salvador; GARCÍA, Vicente. Gene selection and disease prediction from gene expression data using a two-stage hetero-associative memory. Progress in Artificial Intelligence, 2019, vol. 8, no 1, p. 63-71.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s13748-018-0148-6Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
In general, gene expression microarrays consist of a vast number of genes and very few samples, which represents a critical
challenge for disease prediction and diagnosis. This paper develops a two-stage algorithm ... [+]
In general, gene expression microarrays consist of a vast number of genes and very few samples, which represents a critical
challenge for disease prediction and diagnosis. This paper develops a two-stage algorithm that integrates feature selection and
prediction by extending a type of hetero-associative neural networks. In the first level, the algorithm generates the associative
memory, whereas the second level picks the most relevant genes.With the purpose of illustrating the applicability and efficiency
of the method proposed here, we use four different gene expression microarray databases and compare their classification
performance against that of other renowned classifiers built on the whole (original) feature (gene) space. The experimental
results show that the two-stage hetero-associative memory is quite competitive with standard classification models regarding
the overall accuracy, sensitivity and specificity. In addition, it also produces a significant decrease in computational efforts
and an increase in the biological interpretability of microarrays because worthless (irrelevant and/or redundant) genes are
discarded. [-]
Publicado en
Progress in Artificial Intelligence, 8, 2019.Proyecto de investigación
PROME-TEOII/2014/062 ; DSA/103.5/15/7004 ; TIN2013-46522-P.Derechos de acceso
© Springer-Verlag GmbH Germany, part of Springer Nature 2018.
“This is a post-peer-review, pre-copyedit version of an article published in Progress in Artificial Intelligence. The final authenticated version is available online at: https://doi.org/10.1007/s13748-018-0148-6”
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
Aparece en las colecciones
- INIT_Articles [743]
- LSI_Articles [361]