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...
Main Authors: | 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 |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2021-05-01
|
Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-021-22866-x |
Similar Items
-
Association of tamoxifen resistance and lipid reprogramming in breast cancer
by: Susanne Hultsch, et al.
Published: (2018-08-01) -
In vivo immunostaining of hemocyte compartments in Drosophila for live imaging.
by: Gábor Csordás, et al.
Published: (2014-01-01) -
Intelligent image-based in situ single-cell isolation
by: Csilla Brasko, et al.
Published: (2018-01-01) -
The human microbiome: a public health approach
by: Csaba Varga, et al.
Published: (2016-12-01) -
SpheroidPicker for automated 3D cell culture manipulation using deep learning
by: Istvan Grexa, et al.
Published: (2021-07-01)