Extreme learning machines for reverse engineering of gene regulatory networks from expression time series

 

Autores
Rubiolo, Mariano; Milone, Diego Humberto; Stegmayer, Georgina
Tipo de recurso
artículo
Estado
Versión publicada
Año de publicación
2017
País
Argentina
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio
CONICET Digital (CONICET)
Descripción
The reconstruction of gene regulatory networks (GRNs) from genes profiles has a growing interest in bioinformatics for understanding the complex regulatory mechanisms in cellular systems. GRNs explicitly represent the cause-effect of regulation among a group of genes and its reconstruction is today a challenging computational problem. Several methods were proposed, but most of them require different input sources to provide an acceptable prediction. Thus, it is a great challenge to reconstruct a GRN only from temporal gene-expression data. Results: Extreme Learning Machine (ELM) is a new supervised neural model that has gained interest in the last years because of its higher learning rate and better performance than existing supervised models in terms of predictive power. This work proposes a novel approach for GRNs reconstruction in which ELMs are used for modeling the relationships between gene expression time series. Artificial datasets generated with the well-known benchmark tool used in DREAM competitions were used. Real datasets were used for validation of this novel proposal with well-known GRNs underlying the time series. The impact of increasing the size of GRNs was analyzed in detail for the compared methods. The results obtained confirm the superiority of the ELM approach against very recent state-of-the-art methods in the same experimental conditions.
Idioma
inglés
OAI Identifier
oai:ri.conicet.gov.ar:11336/47065
Enlace del recurso
http://hdl.handle.net/11336/47065
Nivel de acceso
Acceso abierto
Materia
EXTREME LEARNING MACHINE
GENE REGULATORY NETWORKS
GENE EXPRESSION
PREDICTION
Ciencias de la Computación
Ciencias de la Computación e Información
CIENCIAS NATURALES Y EXACTAS