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Regression plane concept for analysing continuous cellular processes with machine learning
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DOI：10.1038/s41467-021-22866-x.png)
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Abstract
En 中文
Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool enabling class-free phenotypic supervised machine learning, to describe and explore biological data in a continuous manner. First, we compare traditional classification with regression in a simulated experimental setup. Second, we use our framework to identify genes involved in regulating triglyceride levels in human cells. Subsequently, we analyse a time-lapse dataset on mitosis to demonstrate that the proposed methodology is capable of modelling complex processes at infinite resolution. Finally, we show that hemocyte differentiation in Drosophila melanogaster has continuous characteristics. 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.
Keywords:
HEMOCYTE LINEAGES
DROSOPHILA
STRATEGIES
BLOOD
ENCAPSULATION
COMPARTMENTS
MECHANISMS
FRAMEWORK
SOFTWARE
IMMUNITY
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