A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

Identification of markers of drug response is essential for precision therapy. Here the authors introduce an algorithm that uses prior information about each gene’s importance in AML to identify the most predictive gene-drug associations from transcriptome and drug response data from 30 AML samples....

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Autores principales:	Su-In Lee, Safiye Celik, Benjamin A. Logsdon, Scott M. Lundberg, Timothy J. Martins, Vivian G. Oehler, Elihu H. Estey, Chris P. Miller, Sylvia Chien, Jin Dai, Akanksha Saxena, C. Anthony Blau, Pamela S. Becker
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/ff277a1927e34d3491a2ec37c88816b8
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spelling	oai:doaj.org-article:ff277a1927e34d3491a2ec37c88816b82021-12-02T15:34:40ZA machine learning approach to integrate big data for precision medicine in acute myeloid leukemia10.1038/s41467-017-02465-52041-1723https://doaj.org/article/ff277a1927e34d3491a2ec37c88816b82018-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-017-02465-5https://doaj.org/toc/2041-1723Identification of markers of drug response is essential for precision therapy. Here the authors introduce an algorithm that uses prior information about each gene’s importance in AML to identify the most predictive gene-drug associations from transcriptome and drug response data from 30 AML samples.Su-In LeeSafiye CelikBenjamin A. LogsdonScott M. LundbergTimothy J. MartinsVivian G. OehlerElihu H. EsteyChris P. MillerSylvia ChienJin DaiAkanksha SaxenaC. Anthony BlauPamela S. BeckerNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-13 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Su-In Lee Safiye Celik Benjamin A. Logsdon Scott M. Lundberg Timothy J. Martins Vivian G. Oehler Elihu H. Estey Chris P. Miller Sylvia Chien Jin Dai Akanksha Saxena C. Anthony Blau Pamela S. Becker A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
description	Identification of markers of drug response is essential for precision therapy. Here the authors introduce an algorithm that uses prior information about each gene’s importance in AML to identify the most predictive gene-drug associations from transcriptome and drug response data from 30 AML samples.
format	article
author	Su-In Lee Safiye Celik Benjamin A. Logsdon Scott M. Lundberg Timothy J. Martins Vivian G. Oehler Elihu H. Estey Chris P. Miller Sylvia Chien Jin Dai Akanksha Saxena C. Anthony Blau Pamela S. Becker
author_facet	Su-In Lee Safiye Celik Benjamin A. Logsdon Scott M. Lundberg Timothy J. Martins Vivian G. Oehler Elihu H. Estey Chris P. Miller Sylvia Chien Jin Dai Akanksha Saxena C. Anthony Blau Pamela S. Becker
author_sort	Su-In Lee
title	A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
title_short	A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
title_full	A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
title_fullStr	A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
title_full_unstemmed	A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
title_sort	machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/ff277a1927e34d3491a2ec37c88816b8
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A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

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