Artificial neural network and decision tree facilitated prediction and validation of cytokinin‑auxin induced in vitro organogenesis of sorghum (Sorghum bicolor L.)

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buir.contributor.authorAli, Seyid Amjad
buir.contributor.orcidAli, Seyid Amjad|0000-0001-9250-9020
dc.citation.epage624en_US
dc.citation.spage611
dc.citation.volumeNumber153
dc.contributor.authorAasim, M.
dc.contributor.authorAli, Seyid Amjad
dc.contributor.authorAltaf, M. T.
dc.contributor.authorAli, A.
dc.contributor.authorNadeem, M. A.
dc.contributor.authorBaloch, F. S.
dc.date.accessioned2024-03-07T06:10:54Z
dc.date.available2024-03-07T06:10:54Z
dc.date.issued2023-04-05
dc.departmentComputer Technology and Information Systems
dc.description.abstractIn this study, in vitro regeneration protocol of sorghum (Sorghum bicolor) was successfully established by using direct organogenesis from a mature zygotic embryo explant. The used basal medium encompassed Murashige and Skoog medium (MS) supplemented with 2–4 mg/L Benzylaminopurine (BAP) alone or with 0.25 mg/L Indole butyric acid (IBA) or Naphthalene acetic acid (NAA). Results demonstrated a significant impact of cytokinin-auxin on shoot count (1.24–3.46) and shoot length (2.80–3.47 cm). Maximum shoot count (3.46) and shoot length (3.97 cm) were achieved on the MS medium enriched with 2 mg/L BAP + 0.25 mg/L NAA and 2.0 mg/L BAP, respectively. To ascertain the impact of BAP alone, BAP + IBA, and BAP + NAA, the data were also analyzed by using a factorial regression model. Pareto chart and normal plots were used to check either the positive or negative impact of input variables on output variables. To further explore the association between BAP + IBA and BAP + NAA on shoot count and shoot length, contour and surface plots were also built. Three different artificial intelligence-based models along with four different performance metrics were utilized to validate the predicted results. Multilayer perceptron (MLP) model performed more efficiently (R2 = 0.799 for shoot count and R2 = 0.831 for shoot length) as compared to the decision tree-based algorithms of random forest (RF) – (R2 = 0.779 for shoot count and R2 = 0.786 for shoot length) and extreme gradient boost (XGBoost) – (R2 = 0.768 for shoot count and R2 = 0.781 for shoot length). As plant tissue culture protocol is a powerful tool for genetic engineering and genome editing of crops, integration of different artificial intelligence-based models can lead to improvement of sorghum with the aid of biotechnological tools.
dc.identifier.doi10.1007/s11240-023-02498-3
dc.identifier.eissn1573-5044
dc.identifier.issn0167-6857
dc.identifier.urihttps://hdl.handle.net/11693/114371
dc.language.isoen_US
dc.publisherSpringer Dordrecht
dc.relation.isversionofhttps://dx.doi.org/10.1007/s11240-023-02498-3
dc.rightsCC BY 4.0 DEED (Attribution 4.0 International)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.source.titlePlant Cell, Tissue and Organ Culture
dc.subjectArtifcial intelligence
dc.subjectAuxins
dc.subjectCytokinin
dc.subjectIn vitro
dc.subjectMultilayer perceptron
dc.subjectDecision tree algorithm
dc.titleArtificial neural network and decision tree facilitated prediction and validation of cytokinin‑auxin induced in vitro organogenesis of sorghum (Sorghum bicolor L.)
dc.typeArticle
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