Regression plane concept for analysing continuous cellular processes with machine learning

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorSzkalisity, Abelen_US
dc.contributor.authorPiccinini, Filippoen_US
dc.contributor.authorBeleon, Attilaen_US
dc.contributor.authorBalassa, Tamasen_US
dc.contributor.authorVarga, Istvan Gergelyen_US
dc.contributor.authorMigh, Edeen_US
dc.contributor.authorMolnar, Csabaen_US
dc.contributor.authorPaavolainen, Lassien_US
dc.contributor.authorTimonen, Sannaen_US
dc.contributor.authorBanerjee, Indranilen_US
dc.contributor.authorIkonen, Elinaen_US
dc.contributor.authorYamauchi, Yoheien_US
dc.contributor.authorAndo, Istvanen_US
dc.contributor.authorPeltonen, Jaakkoen_US
dc.contributor.authorPietiäinen, Viljaen_US
dc.contributor.authorHonti, Viktoren_US
dc.contributor.authorHorvath, Peteren_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorProbabilistic Machine Learningen
dc.contributor.groupauthorHelsinki Institute for Information Technology (HIIT)en
dc.contributor.groupauthorProfessorship Kaski Samuelen
dc.contributor.organizationHungarian Academy of Sciencesen_US
dc.contributor.organizationIRCCS Istituto scientifico romagnolo per lo studio e la cura dei tumori - Meldolaen_US
dc.contributor.organizationUniversity of Helsinkien_US
dc.contributor.organizationIndian Institute of Science Education and Research Mohalien_US
dc.contributor.organizationUniversity of Bristolen_US
dc.date.accessioned2021-06-02T06:15:06Z
dc.date.available2021-06-02T06:15:06Z
dc.date.issued2021-12en_US
dc.descriptionFunding Information: The authors thank Antti Lehmussola and Pekka Ruusuvuori (Tampere University of Technology, Finland) for the information provided about the SIMCEP software; Samuli Ripatti and Ida Surakka (FIMM, University of Helsinki, Finland) for their valuable comments on our experiments related to the genes associated with dyslipidemia; Anna Uro (Faculty of Medicine, University of Helsinki) for providing expertise in the biochemical quantification of lipid levels; Mariliina Arjama (FIMM, University of Helsinki, Finland) for technical expertise in cell culture; the FIMM High Throughput Biomedicine Unit for providing access to high throughput robotics and siRNA library and the FIMM High Content Imaging and Analysis unit for HC-imaging (HiLIFE, University of Helsinki and Biocenter Finland); Olli Kallioniemi (FIMM, University of Helsinki, Finland) for support in HC-imaging capabilities and funding; Gabriella Tick and Máté Görbe (BRC, Szeged, Hungary) for their help with the software documentation; the Finnish Grid and Cloud Infrastructure (urn:nbn:fi:research-infras-2016072533) for computational resources; Dóra Bokor (BRC, Szeged, Hungary) for proofreading the manuscript. A.Sz., B.T., A.B., E.M., Cs.M. and P.H. acknowledge support from the Hungarian National Brain Research Program (MTA-SE-NAP B-BIOMAG), from the LENDULET-BIOMAG Grant (2018-342), from the 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), from the H2020 (ERAPERMED-COMPASS, DiscovAIR) and from the Chan Zuckerberg Initiative (Deep Visual Proteomics). A.Sz. and E.I. acknowledges support from University of Helsinki (Centre of Excellence matching funds) and Academy of Finland (project 324929). V.P., L.P. and P.H. acknowledge support from the Finnish TEKES FiDiPro Fellow Grant 40294/13 and FIMM High Content Imaging and Analysis Unit (FIMM-HCA; HiLIFE-HELMI) and Biocenter Finland, Finnish Cancer Society, Juselius Foundation, Academy of Finland Centre of Excellence in Translational Cancer Biology, Kymenlaakso and Finnish Cultural Foundation. V.P. acknowledges University of Helsinki post-doctoral research project grant. F.P. acknowledges support from the Union for International Cancer Control (UICC) for a UICC Yamagiwa-Yoshida (YY) Memorial International Cancer Study Grant (ref: UICC-YY/678329). V.H. and I.A. acknowledge the Hungarian National Research Fund (OTKA NKFI‐2 NN118207). V.H. acknowledges support from the National Research, Development and Innovation Office (OTKA K-131484). L.P. and J.P. acknowledge support from the Academy of Finland, decision numbers 295694, 313748, 327352 and 310552. Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
dc.description.abstractBiological 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.en
dc.description.versionPeer revieweden
dc.format.extent9
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationSzkalisity, A, Piccinini, F, Beleon, A, Balassa, T, Varga, I G, Migh, E, Molnar, C, Paavolainen, L, Timonen, S, Banerjee, I, Ikonen, E, Yamauchi, Y, Ando, I, Peltonen, J, Pietiäinen, V, Honti, V & Horvath, P 2021, ' Regression plane concept for analysing continuous cellular processes with machine learning ', Nature Communications, vol. 12, no. 1, 2532 . https://doi.org/10.1038/s41467-021-22866-xen
dc.identifier.doi10.1038/s41467-021-22866-xen_US
dc.identifier.issn2041-1723
dc.identifier.otherPURE UUID: 3f52036d-3a0e-4536-b101-89182b4f02a9en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/3f52036d-3a0e-4536-b101-89182b4f02a9en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85105357600&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/63281966/Regression_plane_concept.s41467_021_22866_x.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/107893
dc.identifier.urnURN:NBN:fi:aalto-202106027146
dc.language.isoenen
dc.publisherNature Publishing Group
dc.relation.ispartofseriesNature Communicationsen
dc.relation.ispartofseriesVolume 12, issue 1en
dc.rightsopenAccessen
dc.titleRegression plane concept for analysing continuous cellular processes with machine learningen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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