Regression plane concept: analysing continuous cellular processes with machine learning

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.

doi:10.1101/2020.09.01.276089

bioRxiv · 10.1101/2020.09.01.276089

Regression plane concept: analysing continuous cellular processes with machine learning

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.

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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.

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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.. 2020-09-01. Regression plane concept: analysing continuous cellular processes with machine learning. https://doi.org/10.1101/2020.09.01.276089

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