4.8 Article

Regression plane concept for analysing continuous cellular processes with machine learning

Journal

NATURE COMMUNICATIONS
Volume 12, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41467-021-22866-x

Keywords

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Funding

  1. Hungarian National Brain Research Program (MTA-SE-NAP B-BIOMAG)
  2. LENDULET-BIOMAG Grant [2018-342]
  3. European Regional Development Funds [GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00026, GINOP-2.3.2-15-2016-00037]
  4. H2020 (ERAPERMED-COMPASS, DiscovAIR)
  5. Chan Zuckerberg Initiative
  6. University of Helsinki (Centre of Excellence matching funds)
  7. Academy of Finland [324929, 295694, 313748, 327352, 310552]
  8. Finnish TEKES FiDiPro Fellow Grant [40294/13]
  9. FIMM High Content Imaging and Analysis Unit (FIMM-HCA)
  10. Biocenter Finland
  11. Finnish Cancer Society
  12. Juselius Foundation
  13. Academy of Finland Centre of Excellence in Translational Cancer Biology, Kymenlaakso
  14. Finnish Cultural Foundation
  15. University of Helsinki post-doctoral research project grant
  16. Union for International Cancer Control (UICC) [UICC-YY/678329]
  17. Hungarian National Research Fund [OTKA NKFI-2 NN118207]
  18. National Research, Development and Innovation Office [OTKA K-131484]
  19. FIMM High Content Imaging and Analysis Unit (HiLIFE-HELMI)
  20. University of Helsinki, Finland
  21. Academy of Finland (AKA) [313748, 310552, 327352, 324929, 313748, 310552, 295694, 324929, 327352, 295694] Funding Source: Academy of Finland (AKA)

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The use of Regression Plane as a user-friendly discovery tool enables continuous phenotypic supervised machine learning in biological data exploration. The approach has the potential for application in experimental data processing and novel discoveries.
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.

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