A meta-learning approach for genomic survival analysis

RNA-sequencing data from tumours can be used to predict the prognosis of patients. Here, the authors show that a neural network meta-learning approach can be useful for predicting prognosis from a small number of samples.

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Autores principales:	Yeping Lina Qiu, Hong Zheng, Arnout Devos, Heather Selby, Olivier Gevaert
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/42bb85ba63e2410989419cfb8d38bbf2
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id	oai:doaj.org-article:42bb85ba63e2410989419cfb8d38bbf2
record_format	dspace
spelling	oai:doaj.org-article:42bb85ba63e2410989419cfb8d38bbf22021-12-02T10:48:00ZA meta-learning approach for genomic survival analysis10.1038/s41467-020-20167-32041-1723https://doaj.org/article/42bb85ba63e2410989419cfb8d38bbf22020-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-20167-3https://doaj.org/toc/2041-1723RNA-sequencing data from tumours can be used to predict the prognosis of patients. Here, the authors show that a neural network meta-learning approach can be useful for predicting prognosis from a small number of samples.Yeping Lina QiuHong ZhengArnout DevosHeather SelbyOlivier GevaertNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yeping Lina Qiu Hong Zheng Arnout Devos Heather Selby Olivier Gevaert A meta-learning approach for genomic survival analysis
description	RNA-sequencing data from tumours can be used to predict the prognosis of patients. Here, the authors show that a neural network meta-learning approach can be useful for predicting prognosis from a small number of samples.
format	article
author	Yeping Lina Qiu Hong Zheng Arnout Devos Heather Selby Olivier Gevaert
author_facet	Yeping Lina Qiu Hong Zheng Arnout Devos Heather Selby Olivier Gevaert
author_sort	Yeping Lina Qiu
title	A meta-learning approach for genomic survival analysis
title_short	A meta-learning approach for genomic survival analysis
title_full	A meta-learning approach for genomic survival analysis
title_fullStr	A meta-learning approach for genomic survival analysis
title_full_unstemmed	A meta-learning approach for genomic survival analysis
title_sort	meta-learning approach for genomic survival analysis
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/42bb85ba63e2410989419cfb8d38bbf2
work_keys_str_mv	AT yepinglinaqiu ametalearningapproachforgenomicsurvivalanalysis AT hongzheng ametalearningapproachforgenomicsurvivalanalysis AT arnoutdevos ametalearningapproachforgenomicsurvivalanalysis AT heatherselby ametalearningapproachforgenomicsurvivalanalysis AT oliviergevaert ametalearningapproachforgenomicsurvivalanalysis AT yepinglinaqiu metalearningapproachforgenomicsurvivalanalysis AT hongzheng metalearningapproachforgenomicsurvivalanalysis AT arnoutdevos metalearningapproachforgenomicsurvivalanalysis AT heatherselby metalearningapproachforgenomicsurvivalanalysis AT oliviergevaert metalearningapproachforgenomicsurvivalanalysis
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A meta-learning approach for genomic survival analysis

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