Innovation in the Breeding of Common Bean Through a Combined Approach of in vitro Regeneration and Machine Learning Algorithms
Yazarlar (12)
Muhammad Aasim
Sivas Science And Technology University, Türkiye
Sivas Science And Technology University, Türkiye
Ramazan Katirci Sivas Science And Technology University, Türkiye
Faheem Shehzad Baloch
Sivas Science And Technology University, Türkiye
Sivas Science And Technology University, Türkiye
Zemran Mustafa Sivas Science And Technology University, Türkiye
Allah Bakhsh University of The Punjab, Pakistan
Doç. Dr. Muhammad Azhar Nadeem Sivas Science And Technology University, Türkiye
Seyid Amjad Ali Bilkent Üniversitesi, Türkiye
Prof. Dr. Rüştü HATİPOĞLU Çukurova Üniversitesi, Türkiye
Prof. Dr. Vahdettin Çiftçi Bolu Abant İzzet Baysal Üniversitesi, Türkiye
Ephrem Habyarimana International Crops Research Institute For The Semi-Arid Tropics, Hindistan
Prof. Dr. Tolga Karaköy Sivas Science And Technology University, Türkiye
Yong Suk Chung Jeju National University, Güney Kore
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Frontiers in Genetics (Q2) | ||
| Dergi ISSN | 1664-8021 Dergi Bilgileri (2022) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 08-2022 |
| Cilt / Sayı / Sayfa | 13 / 1 / 1–13 | DOI | 10.3389/fgene.2022.897696 |
| Makale Linki | http://dx.doi.org/10.3389/fgene.2022.897696 | ||
| UAK Araştırma Alanları |
Bitkisel Biyoteknoloji
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| 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 |
| artificial neural network | coefficient of determination | in vitro regeneration | machine learning algorithms | mean squared error | plumular apices |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 28 |
| Google Scholar | 6 |