Nature CommunicationsBioinformaticsPeer-reviewed journal article
Regression plane concept: analysing continuous cellular processes with machine learning
Ábel SzkalisityiD, Filippo PiccininiiD, Attila BeleoniD, Tamás Balassa, Gergely I. B. VargaiD, Ede MighiD, Csaba MolnáriD, Lassi PaavolaineniD, Sanna H. TimoneniD, Indranil BanerjeeiD, Elina IkoneniD, Yohei YamauchiiD, István AndóiD, Jaakko PeltoneniD, Vilja M. PietiäineniD, Viktor HontiiD, Péter HorváthiD
Abstract
Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce a novel concept to describe and explore biological data in a continuous manner. We have implemented Regression Plane (RP), the first user-friendly discovery tool enabling class-free phenotypic supervised machine learning.
- Citations
- 18
- FWCI
- 0.94
- Field percentile
- top 29%
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