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Regression plane concept for analysing continuous cellular processes with machine learning

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Τίτλος: Regression plane concept for analysing continuous cellular processes with machine learning
Συγγραφείς: Szkalisity 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., Pietiainen V., Honti V., Horvath P.
Συνεισφορές: Szkalisity 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., Pietiainen V., Honti V., Horvath P.
Έτος έκδοσης: 2021
Συλλογή: IRIS Università degli Studi di Bologna (CRIS - Current Research Information System)
Θεματικοί όροι: Animal, Carcinoma, Hepatocellular, Cell Cycle, Cell Differentiation, Cell Line, Tumor, Drosophila melanogaster, Human, Membrane Protein, Supervised Machine Learning, Biological Phenomena, Cell Physiological Phenomena, Machine Learning
Περιγραφή: 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.
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: ELETTRONICO
Γλώσσα: English
Relation: info:eu-repo/semantics/altIdentifier/pmid/33953203; info:eu-repo/semantics/altIdentifier/wos/WOS:000656478200010; volume:12; issue:1; firstpage:2532; lastpage:2532; numberofpages:1; journal:NATURE COMMUNICATIONS; https://hdl.handle.net/11585/849968
DOI: 10.1038/s41467-021-22866-x
Διαθεσιμότητα: https://hdl.handle.net/11585/849968
https://doi.org/10.1038/s41467-021-22866-x
Rights: info:eu-repo/semantics/openAccess ; license:Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY) ; license:Licenza per Accesso Aperto. Altra tipologia di licenza compatibile con Open Access ; license uri:iris.PUB15 ; license uri:iris.PUB20
Αριθμός Καταχώρησης: edsbas.8E8CD3B0
Βάση Δεδομένων: BASE
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  – Url: https://hdl.handle.net/11585/849968#
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  Data: Regression plane concept for analysing continuous cellular processes with machine learning
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  Data: <searchLink fieldCode="AR" term="%22Szkalisity+A%2E%22">Szkalisity A.</searchLink><br /><searchLink fieldCode="AR" term="%22Piccinini+F%2E%22">Piccinini F.</searchLink><br /><searchLink fieldCode="AR" term="%22Beleon+A%2E%22">Beleon A.</searchLink><br /><searchLink fieldCode="AR" term="%22Balassa+T%2E%22">Balassa T.</searchLink><br /><searchLink fieldCode="AR" term="%22Varga+I%2E+G%2E%22">Varga I. G.</searchLink><br /><searchLink fieldCode="AR" term="%22Migh+E%2E%22">Migh E.</searchLink><br /><searchLink fieldCode="AR" term="%22Molnar+C%2E%22">Molnar C.</searchLink><br /><searchLink fieldCode="AR" term="%22Paavolainen+L%2E%22">Paavolainen L.</searchLink><br /><searchLink fieldCode="AR" term="%22Timonen+S%2E%22">Timonen S.</searchLink><br /><searchLink fieldCode="AR" term="%22Banerjee+I%2E%22">Banerjee I.</searchLink><br /><searchLink fieldCode="AR" term="%22Ikonen+E%2E%22">Ikonen E.</searchLink><br /><searchLink fieldCode="AR" term="%22Yamauchi+Y%2E%22">Yamauchi Y.</searchLink><br /><searchLink fieldCode="AR" term="%22Ando+I%2E%22">Ando I.</searchLink><br /><searchLink fieldCode="AR" term="%22Peltonen+J%2E%22">Peltonen J.</searchLink><br /><searchLink fieldCode="AR" term="%22Pietiainen+V%2E%22">Pietiainen V.</searchLink><br /><searchLink fieldCode="AR" term="%22Honti+V%2E%22">Honti V.</searchLink><br /><searchLink fieldCode="AR" term="%22Horvath+P%2E%22">Horvath P.</searchLink>
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  Data: Szkalisity A.<br />Piccinini F.<br />Beleon A.<br />Balassa T.<br />Varga I.G.<br />Migh E.<br />Molnar C.<br />Paavolainen L.<br />Timonen S.<br />Banerjee I.<br />Ikonen E.<br />Yamauchi Y.<br />Ando I.<br />Peltonen J.<br />Pietiainen V.<br />Honti V.<br />Horvath P.
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  Data: 2021
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  Data: IRIS Università degli Studi di Bologna (CRIS - Current Research Information System)
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  Data: <searchLink fieldCode="DE" term="%22Animal%22">Animal</searchLink><br /><searchLink fieldCode="DE" term="%22Carcinoma%22">Carcinoma</searchLink><br /><searchLink fieldCode="DE" term="%22Hepatocellular%22">Hepatocellular</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+Cycle%22">Cell Cycle</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+Differentiation%22">Cell Differentiation</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+Line%22">Cell Line</searchLink><br /><searchLink fieldCode="DE" term="%22Tumor%22">Tumor</searchLink><br /><searchLink fieldCode="DE" term="%22Drosophila+melanogaster%22">Drosophila melanogaster</searchLink><br /><searchLink fieldCode="DE" term="%22Human%22">Human</searchLink><br /><searchLink fieldCode="DE" term="%22Membrane+Protein%22">Membrane Protein</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+Machine+Learning%22">Supervised Machine Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+Phenomena%22">Biological Phenomena</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+Physiological+Phenomena%22">Cell Physiological Phenomena</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+Learning%22">Machine Learning</searchLink>
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  Data: 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.
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  Data: info:eu-repo/semantics/altIdentifier/pmid/33953203; info:eu-repo/semantics/altIdentifier/wos/WOS:000656478200010; volume:12; issue:1; firstpage:2532; lastpage:2532; numberofpages:1; journal:NATURE COMMUNICATIONS; https://hdl.handle.net/11585/849968
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  Data: 10.1038/s41467-021-22866-x
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  Data: https://hdl.handle.net/11585/849968<br />https://doi.org/10.1038/s41467-021-22866-x
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  Data: info:eu-repo/semantics/openAccess ; license:Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY) ; license:Licenza per Accesso Aperto. Altra tipologia di licenza compatibile con Open Access ; license uri:iris.PUB15 ; license uri:iris.PUB20
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