KBoost: a new method to infer gene regulatory networks from gene expression data

Abstract Reconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthe...

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Autores principales:	Luis F. Iglesias-Martinez, Barbara De Kegel, Walter Kolch
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b9e71a6b6eac4a62b8fa270b92a39d84
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spelling	oai:doaj.org-article:b9e71a6b6eac4a62b8fa270b92a39d842021-12-02T16:30:10ZKBoost: a new method to infer gene regulatory networks from gene expression data10.1038/s41598-021-94919-62045-2322https://doaj.org/article/b9e71a6b6eac4a62b8fa270b92a39d842021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94919-6https://doaj.org/toc/2045-2322Abstract Reconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.Luis F. Iglesias-MartinezBarbara De KegelWalter KolchNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Luis F. Iglesias-Martinez Barbara De Kegel Walter Kolch KBoost: a new method to infer gene regulatory networks from gene expression data
description	Abstract Reconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.
format	article
author	Luis F. Iglesias-Martinez Barbara De Kegel Walter Kolch
author_facet	Luis F. Iglesias-Martinez Barbara De Kegel Walter Kolch
author_sort	Luis F. Iglesias-Martinez
title	KBoost: a new method to infer gene regulatory networks from gene expression data
title_short	KBoost: a new method to infer gene regulatory networks from gene expression data
title_full	KBoost: a new method to infer gene regulatory networks from gene expression data
title_fullStr	KBoost: a new method to infer gene regulatory networks from gene expression data
title_full_unstemmed	KBoost: a new method to infer gene regulatory networks from gene expression data
title_sort	kboost: a new method to infer gene regulatory networks from gene expression data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b9e71a6b6eac4a62b8fa270b92a39d84
work_keys_str_mv	AT luisfiglesiasmartinez kboostanewmethodtoinfergeneregulatorynetworksfromgeneexpressiondata AT barbaradekegel kboostanewmethodtoinfergeneregulatorynetworksfromgeneexpressiondata AT walterkolch kboostanewmethodtoinfergeneregulatorynetworksfromgeneexpressiondata
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KBoost: a new method to infer gene regulatory networks from gene expression data

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