Innovation in the breeding of common bean through a combined approach of in vitro regeneration and machine learning algorithms

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dc.authorid0000-0003-2448-011Xen_US
dc.authorid0000-0002-7470-0080en_US
dc.authorid0000-0002-1754-6320en_US
dc.authorid0000-0003-0547-9527en_US
dc.contributor.authorAasim, Muhammad
dc.contributor.authorKatırcı, Ramazan
dc.contributor.authorBaloch, Faheem Shehzad
dc.contributor.authorMustafa, Zemran
dc.contributor.authorBakhsh, Allah
dc.contributor.authorÇiftçi, Vahdettin
dc.date.accessioned2023-09-27T06:52:12Z
dc.date.available2023-09-27T06:52:12Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Ziraat Fakültesi, Tarla Bitkileri Bölümüen_US
dc.descriptionBasic Science Research Program supported this research through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2019R1A6A1A11052070).en_US
dc.description.abstractCommon bean is considered a recalcitrant crop for in vitro regeneration and needs a repeatable and efficient in vitro regeneration protocol for its improvement through biotechnological approaches. In this study, the establishment of efficient and reproducible in vitro regeneration followed by predicting and optimizing through machine learning (ML) models, such as artificial neural network algorithms, was performed. Mature embryos of common bean were pretreated with 5, 10, and 20 mg/L benzylaminopurine (BAP) for 20 days followed by isolation of plumular apice for in vitro regeneration and cultured on a post-treatment medium containing 0.25, 0.50, 1.0, and 1.50 mg/L BAP for 8 weeks. Plumular apice explants pretreated with 20 mg/L BAP exerted a negative impact and resulted in minimum shoot regeneration frequency and shoot count, but produced longer shoots. All output variables (shoot regeneration frequency, shoot counts, and shoot length) increased significantly with the enhancement of BAP concentration in the post-treatment medium. Interaction of the pretreatment x post-treatment medium revealed the need for a specific combination for inducing a high shoot regeneration frequency. Higher shoot count and shoot length were achieved from the interaction of 5 mg/L BAP x 1.00 mg/L BAP followed by 10 mg/L BAP x 1.50 mg/L BAP and 20 mg/L BAP x 1.50 mg/L BAP. The evaluation of data through ML models revealed that R-2 values ranged from 0.32 to 0.58 (regeneration), 0.01 to 0.22 (shoot counts), and 0.18 to 0.48 (shoot length). On the other hand, the mean squared error values ranged from 0.0596 to 0.0965 for shoot regeneration, 0.0327 to 0.0412 for shoot count, and 0.0258 to 0.0404 for shoot length from all ML models. Among the utilized models, the multilayer perceptron model provided a better prediction and optimization for all output variables, compared to other models. The achieved results can be employed for the prediction and optimization of plant tissue culture protocols used for biotechnological approaches in a breeding program of common beans.en_US
dc.description.sponsorshipNational Research Foundation of Korea (NRF) - Ministry of Education [2019R1A6A1A11052070]en_US
dc.identifier.citationAasim, M., Katirci, R., Baloch, F. S., Mustafa, Z., Bakhsh, A., Nadeem, M. A., ... & Chung, Y. S. (2022). Innovation in the breeding of common bean through a combined approach of in vitro regeneration and machine learning algorithms. Frontiers in Genetics, 13, 897696.en_US
dc.identifier.doi10.3389/fgene.2022.897696
dc.identifier.endpage13en_US
dc.identifier.issn1664-8021
dc.identifier.pmid36092939en_US
dc.identifier.scopus2-s2.0-85138091216en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.3389/fgene.2022.897696
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11748
dc.identifier.volume13en_US
dc.identifier.wosWOS:000850730700001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorÇiftçi, Vahdettin
dc.language.isoenen_US
dc.publisherFrontiers Media SAen_US
dc.relation.ispartofFrontiers in Geneticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine Learning Algorithmsen_US
dc.subjectArtificial Neural Networken_US
dc.subjectIn Vitro Regenerationen_US
dc.subjectVigna-Unguiculata L.en_US
dc.subjectArachis-Hypogaea L.en_US
dc.subjectShoot Regenerationen_US
dc.titleInnovation in the breeding of common bean through a combined approach of in vitro regeneration and machine learning algorithmsen_US
dc.typeArticleen_US

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