Improving gene function predictions using independent transcriptional components

Our understanding of the function of many transcripts is still incomplete, limiting the interpretability of transcriptomic data. Here the authors use consensus-independent component analysis, together with a guilt-by-association approach, to improve the prediction of gene function.

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Autores principales:	Carlos G. Urzúa-Traslaviña, Vincent C. Leeuwenburgh, Arkajyoti Bhattacharya, Stefan Loipfinger, Marcel A. T. M. van Vugt, Elisabeth G. E. de Vries, Rudolf S. N. Fehrmann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4a0123198db1432b8cd7b6fe248572ba
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spelling	oai:doaj.org-article:4a0123198db1432b8cd7b6fe248572ba2021-12-02T15:52:37ZImproving gene function predictions using independent transcriptional components10.1038/s41467-021-21671-w2041-1723https://doaj.org/article/4a0123198db1432b8cd7b6fe248572ba2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21671-whttps://doaj.org/toc/2041-1723Our understanding of the function of many transcripts is still incomplete, limiting the interpretability of transcriptomic data. Here the authors use consensus-independent component analysis, together with a guilt-by-association approach, to improve the prediction of gene function.Carlos G. Urzúa-TraslaviñaVincent C. LeeuwenburghArkajyoti BhattacharyaStefan LoipfingerMarcel A. T. M. van VugtElisabeth G. E. de VriesRudolf S. N. FehrmannNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Carlos G. Urzúa-Traslaviña Vincent C. Leeuwenburgh Arkajyoti Bhattacharya Stefan Loipfinger Marcel A. T. M. van Vugt Elisabeth G. E. de Vries Rudolf S. N. Fehrmann Improving gene function predictions using independent transcriptional components
description	Our understanding of the function of many transcripts is still incomplete, limiting the interpretability of transcriptomic data. Here the authors use consensus-independent component analysis, together with a guilt-by-association approach, to improve the prediction of gene function.
format	article
author	Carlos G. Urzúa-Traslaviña Vincent C. Leeuwenburgh Arkajyoti Bhattacharya Stefan Loipfinger Marcel A. T. M. van Vugt Elisabeth G. E. de Vries Rudolf S. N. Fehrmann
author_facet	Carlos G. Urzúa-Traslaviña Vincent C. Leeuwenburgh Arkajyoti Bhattacharya Stefan Loipfinger Marcel A. T. M. van Vugt Elisabeth G. E. de Vries Rudolf S. N. Fehrmann
author_sort	Carlos G. Urzúa-Traslaviña
title	Improving gene function predictions using independent transcriptional components
title_short	Improving gene function predictions using independent transcriptional components
title_full	Improving gene function predictions using independent transcriptional components
title_fullStr	Improving gene function predictions using independent transcriptional components
title_full_unstemmed	Improving gene function predictions using independent transcriptional components
title_sort	improving gene function predictions using independent transcriptional components
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4a0123198db1432b8cd7b6fe248572ba
work_keys_str_mv	AT carlosgurzuatraslavina improvinggenefunctionpredictionsusingindependenttranscriptionalcomponents AT vincentcleeuwenburgh improvinggenefunctionpredictionsusingindependenttranscriptionalcomponents AT arkajyotibhattacharya improvinggenefunctionpredictionsusingindependenttranscriptionalcomponents AT stefanloipfinger improvinggenefunctionpredictionsusingindependenttranscriptionalcomponents AT marcelatmvanvugt improvinggenefunctionpredictionsusingindependenttranscriptionalcomponents AT elisabethgedevries improvinggenefunctionpredictionsusingindependenttranscriptionalcomponents AT rudolfsnfehrmann improvinggenefunctionpredictionsusingindependenttranscriptionalcomponents
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Improving gene function predictions using independent transcriptional components

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