Light-emitting diodes induced in vitro regeneration of Alternanthera reineckii mini and validation via machine learning algorithms

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buir.contributor.authorAli, Seyid Amjad
buir.contributor.orcidAli, Seyid Amjad|0000-0001-9250-9020
dc.citation.epage825en_US
dc.citation.issueNumber5en_US
dc.citation.spage816en_US
dc.citation.volumeNumber58en_US
dc.contributor.authorAasim, M.
dc.contributor.authorAli, Seyid Amjad
dc.date.accessioned2023-02-22T07:54:02Z
dc.date.available2023-02-22T07:54:02Z
dc.date.issued2022-10-22
dc.departmentInformation Systems and Technologiesen_US
dc.description.abstractOptimization of in vitro regeneration protocol using multiple input variables is highly significant, and can be achieved by validating the data using machine learning algorithms. Shoot tip and nodal segment explants of Alternanthera reineckii mini were inoculated on Murashige and Skoog (MS) medium enriched with different concentrations of benzylaminopurine (BAP), and cultured under five different monochromic light-emitting diodes (LEDs). The attained results were validated through the application of four different supervised machine learning models (RF, XGBoost, KNN, and GP). The prediction of the data were validated by using regression coefficient (R2), mean squared error (MSE), and mean absolute percentage error (MAPE) performance metrics. Results revealed R2 values of 0.61 and 0.59 for shoot counts and shoot length, respectively. The results of MSE were registered between 3.48–5.42 for shoot count and 0.40–0.74 for shoot length, whereas, 28.9–35.1% and 13.2–18.4% MAPE values were recorded for both shoot count and shoot length. Among the utilized models, the RF model validated and predicted the results more accurately, followed by the XGBoost model for both output variables. The results confirm that ML models can be used for data validation, and opens a new era of employing ML modeling in plant tissue culture of other economically important plants. Graphical abstract: Schematic structure presenting input features and outputs together with ML models, used validation and performance metrics [Figure not available: see fulltext.]. © 2022, The Society for In Vitro Biology.en_US
dc.identifier.doi10.1007/s11627-022-10312-6en_US
dc.identifier.issn1054-5476
dc.identifier.urihttp://hdl.handle.net/11693/111599
dc.language.isoEnglishen_US
dc.publisherSpringeren_US
dc.relation.isversionofhttps://doi.org/10.1007/s11627-022-10312-6en_US
dc.source.titleIn Vitro Cellular and Developmental Biology - Planten_US
dc.subjectAlternanthera reineckiien_US
dc.subjectANOVAen_US
dc.subjectAquatic planten_US
dc.subjectSupervised machine learningen_US
dc.titleLight-emitting diodes induced in vitro regeneration of Alternanthera reineckii mini and validation via machine learning algorithmsen_US
dc.typeArticleen_US
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