Extreme learning machines for reverse engineering of gene regulatory networks from expression time series-Reference-Cited by-同舟云学术

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

Published:2017-11-22 Issue:7 Volume:34 Page:1253-1260
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Rubiolo M¹²,Milone D H¹,Stegmayer G¹^ORCID

Affiliation:

1. Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe, Argentina

2. Center of Research and Development of Information System Engineering, CIDISI, System Engineering Department, UTN-FRSF, Santa Fe, Argentina

Funder

National Scientific and Technical Research Council

CONICET

UNL

National Technological University

UTN

ANPCyT

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/article-pdf/34/7/1253/25119632/btx730.pdf

Reference22 articles.

1. A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches;Cantone;Cell,2009

2. De novo reconstruction of gene regulatory networks from time series data, an approach based on formal methods;Ceccarelli;Methods,2014

3. A review on the computational approaches for gene regulatory network construction;Chai;Comput. Biol. Med,2014

4. Statistical comparisons of classifiers over multiple data sets;Demsar;J. Mach. Learn. Res,2006

5. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles;Faith;PLoS Biol,2007

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