Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. Here, the authors present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data...

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Autores principales:	JungHo Kong, Heetak Lee, Donghyo Kim, Seong Kyu Han, Doyeon Ha, Kunyoo Shin, Sanguk Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/bc2d4c908ac14d54ad3b9ed62cdef10f
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spelling	oai:doaj.org-article:bc2d4c908ac14d54ad3b9ed62cdef10f2021-12-02T17:31:07ZNetwork-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients10.1038/s41467-020-19313-82041-1723https://doaj.org/article/bc2d4c908ac14d54ad3b9ed62cdef10f2020-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19313-8https://doaj.org/toc/2041-1723Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. Here, the authors present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data.JungHo KongHeetak LeeDonghyo KimSeong Kyu HanDoyeon HaKunyoo ShinSanguk KimNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q JungHo Kong Heetak Lee Donghyo Kim Seong Kyu Han Doyeon Ha Kunyoo Shin Sanguk Kim Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
description	Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. Here, the authors present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data.
format	article
author	JungHo Kong Heetak Lee Donghyo Kim Seong Kyu Han Doyeon Ha Kunyoo Shin Sanguk Kim
author_facet	JungHo Kong Heetak Lee Donghyo Kim Seong Kyu Han Doyeon Ha Kunyoo Shin Sanguk Kim
author_sort	JungHo Kong
title	Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
title_short	Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
title_full	Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
title_fullStr	Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
title_full_unstemmed	Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
title_sort	network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/bc2d4c908ac14d54ad3b9ed62cdef10f
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Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

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