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Inferring gene expression networks with hubs using a degree weighted Lasso approach

Sulaimanov, N. ; Koeppl, H. ; Burdet, F. ; Ibberson, M. ; Pagni, M. ; Kumar, S. (2018)
Inferring gene expression networks with hubs using a degree weighted Lasso approach.
In: Bioinformatics (Oxford, England), bty716
doi: 10.1093/bioinformatics/bty716
Article, Bibliographie

Abstract

Motivation Genome-scale gene networks contain regulatory genes called hubs that have many interaction partners. These genes usually play an essential role in gene regulation and cellular processes. Despite recent advancements in high-throughput technology, inferring gene networks with hub genes from high-dimensional data still remains a challenging problem. Novel statistical network inference methods are needed for efficient and accurate reconstruction of hub networks from high-dimensional data.

Results To address this challenge we propose DW-Lasso, a degree weighted Lasso (least absolute shrinkage and selection operator) method which infers gene networks with hubs efficiently under the low sample size setting. Our network reconstruction approach is formulated as a two stage procedure: first, the degree of networks is estimated iteratively, and second, the gene regulatory network is reconstructed using degree information. A useful property of the proposed method is that it naturally favours the accumulation of neighbours around hub genes and thereby helps in accurate modeling of the high-throughput data under the assumption that the underlying network exhibits hub structure. In a simulation study, we demonstrate good predictive performance of the proposed method in comparison to traditional Lasso type methods in inferring hub and scale-free graphs. We show the effectiveness of our method in an application to microarray data of E.coli and RNA sequencing data of Kidney Clear Cell Carcinoma from The Cancer Genome Atlas datasets.

Item Type: Article
Erschienen: 2018
Creators: Sulaimanov, N. ; Koeppl, H. ; Burdet, F. ; Ibberson, M. ; Pagni, M. ; Kumar, S.
Type of entry: Bibliographie
Title: Inferring gene expression networks with hubs using a degree weighted Lasso approach
Language: English
Date: 28 August 2018
Publisher: Oxford University Press
Journal or Publication Title: Bioinformatics (Oxford, England)
Volume of the journal: bty716
DOI: 10.1093/bioinformatics/bty716
URL / URN: https://academic.oup.com/bioinformatics/advance-article/doi/...
Abstract:

Motivation Genome-scale gene networks contain regulatory genes called hubs that have many interaction partners. These genes usually play an essential role in gene regulation and cellular processes. Despite recent advancements in high-throughput technology, inferring gene networks with hub genes from high-dimensional data still remains a challenging problem. Novel statistical network inference methods are needed for efficient and accurate reconstruction of hub networks from high-dimensional data.

Results To address this challenge we propose DW-Lasso, a degree weighted Lasso (least absolute shrinkage and selection operator) method which infers gene networks with hubs efficiently under the low sample size setting. Our network reconstruction approach is formulated as a two stage procedure: first, the degree of networks is estimated iteratively, and second, the gene regulatory network is reconstructed using degree information. A useful property of the proposed method is that it naturally favours the accumulation of neighbours around hub genes and thereby helps in accurate modeling of the high-throughput data under the assumption that the underlying network exhibits hub structure. In a simulation study, we demonstrate good predictive performance of the proposed method in comparison to traditional Lasso type methods in inferring hub and scale-free graphs. We show the effectiveness of our method in an application to microarray data of E.coli and RNA sequencing data of Kidney Clear Cell Carcinoma from The Cancer Genome Atlas datasets.

Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Date Deposited: 30 Aug 2018 09:39
Last Modified: 23 Sep 2021 14:30
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