Innovation in the Breeding of Common Bean Through a Combined Approach of in vitro Regeneration and Machine Learning Algorithms - Aasim, Muhammad; Katirci, Ramazan; Baloch, Faheem Shehzad; Mustafa, Zemran; Bakhsh, Allah; Nadeem, Muhammad Azhar; Ali, Seyid Amjad; Hatipoğlu, Rüştü; Çiftçi, Vahdettin; Habyarimana, Ephrem; Karaköy, Tolga; Chung, Yong Suk (Frontiers in Genetics, 2022)
Common bean is considered a recalcitrant crop for in vitro regeneration and needs repeatable and efficient in vitro regeneration protocol for its improvement through biotechnological approaches. The establishment of efficient and reproducible in vitro regeneration followed by predicting and optimizing through machine learning (ML) models such as artificial neural network (ANN) 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 apices 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 apices explants pretreated with 20 mg/L BAP exerted 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 pretreatment × post-treatment medium revealed the need for a specific combination for inducing 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 valuation of data through ML models revealed R2 and MSE values ranged from 0.32-0.58 (regeneration), 0.01-0.22 (shoot counts), and 0.18-0.48 for shoot length. On the other hand, The MSE values ranged from 0.0596-0.0965 for shoot regeneration, 0.0327-0.0412 for shoot count, and 0.0258-0.0404 for shoot length from all ML models. Among the utilized models, the MLP model provided a better prediction and optimization of 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 bean.