INVESTIGADORES
MILONE Diego Humberto
artículos
Título:
Mining gene regulatory networks by neural modeling of expression time-series (IF 1.438)
Autor/es:
RUBIOLO, M.; STEGMAYER, G.; MILONE, D.H.
Revista:
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Editorial:
IEEE COMPUTER SOC
Referencias:
Lugar: Los Alamitos, CA, USA; Año: 2015 vol. 12 p. 1365 - 1373
ISSN:
1545-5963
Resumen:
Abstract?Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a of neural networks for obtaining a gene regulatory network from a gene expression dataset. They pool are used for modeling each possible interaction between pairs of genes in the dataset, and a set of is applied to mining rules accurately effectiveness detect forF discovering the subjacent regulatory relations networks among genes. from a proper The results modeling obtained of the on temporal artificialdynamics and real datasets of gene expression confirm the profiles.
