Innovation in the Breeding of Common Bean Through a Combined Approach of in vitro Regeneration and Machine Learning Algorithms
 
Yazarlar (12)
Muhammad Aasım
Türkiye
Faheem Shahzad Baloch Sivas Bilim ve Teknoloji Üniversitesi, Türkiye
Allah Bakhsh
Türkiye
Muhammed Azhar Nadeem
Sivas Bilim ve Teknoloji Üniversitesi, Türkiye
Prof. Dr. Rüştü HATİPOĞLU Kırşehir Ahi Evran Üniversitesi, Türkiye
Vahdettin Çiftçi Bolu Abant İzzet Baysal Üniversitesi, Türkiye
Yong Suk Chung
Makale Türü Açık Erişim Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
Dergi Adı Frontiers in Genetics (Q1)
Dergi ISSN 1664-8021 Wos Dergi Scopus Dergi
Dergi Tarandığı Indeksler SCI-Expanded
Makale Dili İngilizce Basım Tarihi 08-2022
Kabul Tarihi 12-04-2026 Yayınlanma Tarihi
Cilt / Sayı / Sayfa 13 / 897696 / 1–13 DOI 10.3389/fgene.2022.897696
Makale Linki http://dx.doi.org/10.3389/fgene.2022.897696
Özet
Common 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 × 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 × 1.00 mg/L BAP followed by 10 mg/L BAP × 1.50 mg/L BAP and 20 mg/L BAP × 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 …
Anahtar Kelimeler
machine learning algorithms | artificial neural network | in vitro regeneration | plumular apices | coefficient of determination | mean squared error
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Google Scholar 35
Web of Science 25
Innovation in the Breeding of Common Bean Through a Combined Approach of in vitro Regeneration and Machine Learning Algorithms

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