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Regression plane concept: analysing continuous cellular processes with machine learning
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.09.01.276089v1?rss=1
Authors: Abel, S., Piccinini, F., Beleon, A., Balassa, T., Varga, I. G., Migh, E., Paavolainen, L., Timonen, S., Banerjee, I., Yamauchi, Y., Ando, I., Peltonen, J., Pietiainen, V., Honti, V., Horvath, P.
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.
Copy rights belong to original authors. Visit the link for more info
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By Multimodal LLCLink to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.09.01.276089v1?rss=1
Authors: Abel, S., Piccinini, F., Beleon, A., Balassa, T., Varga, I. G., Migh, E., Paavolainen, L., Timonen, S., Banerjee, I., Yamauchi, Y., Ando, I., Peltonen, J., Pietiainen, V., Honti, V., Horvath, P.
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.
Copy rights belong to original authors. Visit the link for more info