Regression plane concept for analysing continuous cellular processes with machine learning

High-content screening prompted the development of software enabling discrete phenotypic analysis of single cells. Here, the authors show that supervised continuous machine learning can drive novel discoveries in diverse imaging experiments and present the Regression Plane module of Advanced Cell Cl...

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Autores principales:	Abel Szkalisity, Filippo Piccinini, Attila Beleon, Tamas Balassa, Istvan Gergely Varga, Ede Migh, Csaba Molnar, Lassi Paavolainen, Sanna Timonen, Indranil Banerjee, Elina Ikonen, Yohei Yamauchi, Istvan Ando, Jaakko Peltonen, Vilja Pietiäinen, Viktor Honti, Peter Horvath
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f49f0d2d8b5f4b588375e657d0654bfa
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spelling	oai:doaj.org-article:f49f0d2d8b5f4b588375e657d0654bfa2021-12-02T14:29:13ZRegression plane concept for analysing continuous cellular processes with machine learning10.1038/s41467-021-22866-x2041-1723https://doaj.org/article/f49f0d2d8b5f4b588375e657d0654bfa2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22866-xhttps://doaj.org/toc/2041-1723High-content screening prompted the development of software enabling discrete phenotypic analysis of single cells. Here, the authors show that supervised continuous machine learning can drive novel discoveries in diverse imaging experiments and present the Regression Plane module of Advanced Cell Classifier.Abel SzkalisityFilippo PiccininiAttila BeleonTamas BalassaIstvan Gergely VargaEde MighCsaba MolnarLassi PaavolainenSanna TimonenIndranil BanerjeeElina IkonenYohei YamauchiIstvan AndoJaakko PeltonenVilja PietiäinenViktor HontiPeter HorvathNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Abel Szkalisity Filippo Piccinini Attila Beleon Tamas Balassa Istvan Gergely Varga Ede Migh Csaba Molnar Lassi Paavolainen Sanna Timonen Indranil Banerjee Elina Ikonen Yohei Yamauchi Istvan Ando Jaakko Peltonen Vilja Pietiäinen Viktor Honti Peter Horvath Regression plane concept for analysing continuous cellular processes with machine learning
description	High-content screening prompted the development of software enabling discrete phenotypic analysis of single cells. Here, the authors show that supervised continuous machine learning can drive novel discoveries in diverse imaging experiments and present the Regression Plane module of Advanced Cell Classifier.
format	article
author	Abel Szkalisity Filippo Piccinini Attila Beleon Tamas Balassa Istvan Gergely Varga Ede Migh Csaba Molnar Lassi Paavolainen Sanna Timonen Indranil Banerjee Elina Ikonen Yohei Yamauchi Istvan Ando Jaakko Peltonen Vilja Pietiäinen Viktor Honti Peter Horvath
author_facet	Abel Szkalisity Filippo Piccinini Attila Beleon Tamas Balassa Istvan Gergely Varga Ede Migh Csaba Molnar Lassi Paavolainen Sanna Timonen Indranil Banerjee Elina Ikonen Yohei Yamauchi Istvan Ando Jaakko Peltonen Vilja Pietiäinen Viktor Honti Peter Horvath
author_sort	Abel Szkalisity
title	Regression plane concept for analysing continuous cellular processes with machine learning
title_short	Regression plane concept for analysing continuous cellular processes with machine learning
title_full	Regression plane concept for analysing continuous cellular processes with machine learning
title_fullStr	Regression plane concept for analysing continuous cellular processes with machine learning
title_full_unstemmed	Regression plane concept for analysing continuous cellular processes with machine learning
title_sort	regression plane concept for analysing continuous cellular processes with machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f49f0d2d8b5f4b588375e657d0654bfa
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Regression plane concept for analysing continuous cellular processes with machine learning

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