Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/33806
Title: | GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks | Authors: | Moerman, T Santos, SA Gonzalez-Blas, CB Simm, J Moreau, Y AERTS, Jan Aerts, S |
Editors: | Kelso, Janet | Issue Date: | 2018 | Publisher: | OXFORD UNIV PRESS | Source: | BIOINFORMATICS, 35 (12) , p. 2159 -2161 | Abstract: | Inferring a Gene Regulatory Network (GRN) from gene expression data is a computationally expensive task, exacerbated by increasing data sizes due to advances in high-throughput gene profiling technology, such as single-cell RNA-seq. To equip researchers with a toolset to infer GRNs from large expression datasets, we propose GRNBoost2 and the Arboreto framework. GRNBoost2 is an efficient algorithm for regulatory network inference using gradient boosting, based on the GENIE3 architecture. Arboreto is a computational framework that scales up GRN inference algorithms complying with this architecture. Arboreto includes both GRNBoost2 and an improved implementation of GENIE3, as a user-friendly open source Python package. | Keywords: | Computational Biology;Gene Expression;Software;Algorithms;Gene Regulatory Networks | Document URI: | http://hdl.handle.net/1942/33806 | ISBN: | 14602059 13674803 | ISSN: | 1367-4803 | e-ISSN: | 1367-4811 | DOI: | 10.1093/bioinformatics/bty916 | ISI #: | 000474844600025 | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
Show full item record
WEB OF SCIENCETM
Citations
242
checked on Oct 19, 2024
Page view(s)
52
checked on Jun 14, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.