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

María Florencia Rubiolo | Zendy; Diego H. Milone | Zendy; Georgina Stegmayer | Zendy

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

Author(s) -

María Florencia Rubiolo,

Diego H. Milone,

Georgina Stegmayer

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx730

Subject(s) - computer science , benchmark (surveying) , machine learning , artificial intelligence , extreme learning machine , reverse engineering , source code , gene regulatory network , data mining , deep learning , time series , artificial neural network , gene , gene expression , biology , biochemistry , programming language , geography , operating system , geodesy

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.

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