Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design

Inoue, Takeuchi and colleagues propose a machine learning-based protocol to screen rhodopsins for their likelihood to be red-shifted. After experimental verification, their tool shows remarkable success at identifying rhodopsins that showed red-shift gains.

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Autores principales:	Keiichi Inoue, Masayuki Karasuyama, Ryoko Nakamura, Masae Konno, Daichi Yamada, Kentaro Mannen, Takashi Nagata, Yu Inatsu, Hiromu Yawo, Kei Yura, Oded Béjà, Hideki Kandori, Ichiro Takeuchi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/a7253888b2914466b0761002b36b7c01
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spelling	oai:doaj.org-article:a7253888b2914466b0761002b36b7c012021-12-02T11:39:38ZExploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design10.1038/s42003-021-01878-92399-3642https://doaj.org/article/a7253888b2914466b0761002b36b7c012021-03-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-01878-9https://doaj.org/toc/2399-3642Inoue, Takeuchi and colleagues propose a machine learning-based protocol to screen rhodopsins for their likelihood to be red-shifted. After experimental verification, their tool shows remarkable success at identifying rhodopsins that showed red-shift gains.Keiichi InoueMasayuki KarasuyamaRyoko NakamuraMasae KonnoDaichi YamadaKentaro MannenTakashi NagataYu InatsuHiromu YawoKei YuraOded BéjàHideki KandoriIchiro TakeuchiNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Keiichi Inoue Masayuki Karasuyama Ryoko Nakamura Masae Konno Daichi Yamada Kentaro Mannen Takashi Nagata Yu Inatsu Hiromu Yawo Kei Yura Oded Béjà Hideki Kandori Ichiro Takeuchi Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design
description	Inoue, Takeuchi and colleagues propose a machine learning-based protocol to screen rhodopsins for their likelihood to be red-shifted. After experimental verification, their tool shows remarkable success at identifying rhodopsins that showed red-shift gains.
format	article
author	Keiichi Inoue Masayuki Karasuyama Ryoko Nakamura Masae Konno Daichi Yamada Kentaro Mannen Takashi Nagata Yu Inatsu Hiromu Yawo Kei Yura Oded Béjà Hideki Kandori Ichiro Takeuchi
author_facet	Keiichi Inoue Masayuki Karasuyama Ryoko Nakamura Masae Konno Daichi Yamada Kentaro Mannen Takashi Nagata Yu Inatsu Hiromu Yawo Kei Yura Oded Béjà Hideki Kandori Ichiro Takeuchi
author_sort	Keiichi Inoue
title	Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design
title_short	Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design
title_full	Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design
title_fullStr	Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design
title_full_unstemmed	Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design
title_sort	exploration of natural red-shifted rhodopsins using a machine learning-based bayesian experimental design
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a7253888b2914466b0761002b36b7c01
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Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design

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