Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L)

Aasım, Muhammad; Akın, Fatma Ebru; Ali, Seyid Amjad; Taşkın, Mehmet Burak; Colak, Muslume Sevba; Khawar, Khalid Mahmood

doi:10.1007/s12298-023-01282-z

Cited by 11 publications

(13 citation statements)

References 73 publications

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“…This investigation encompassed the pursuit of output variables (namely, CI, ECI, REC, RE, and PN) by leveraging input factors and molecular values, which were subsequently subjected to analysis through the prism of machine learning (ML) algorithms. The aptitude of ML algorithms in the assessment and validation of projected output variables finds resonance in their inherent capacity to incorporate the input parameters underlying the outcomes [ 24 , 25 , 59 , 60 ]. Notably, the past years have witnessed the burgeoning utilization of ML models for data validation within in vitro regeneration research, manifested across a diverse array of inquiries characterized by distinct objectives and focal points [ 25 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

Investigation of the Influence of Polyamines on Mature Embryo Culture and DNA Methylation of Wheat (Triticum aestivum L.) Using the Machine Learning Algorithm Method

Eren,

Türkoğlu,

Haliloğlu

et al. 2023

Plants

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Numerous factors can impact the efficiency of callus formation and in vitro regeneration in wheat cultures through the introduction of exogenous polyamines (PAs). The present study aimed to investigate in vitro plant regeneration and DNA methylation patterns utilizing the inter-primer binding site (iPBS) retrotransposon and coupled restriction enzyme digestion–iPBS (CRED–iPBS) methods in wheat. This investigation involved the application of distinct types of PAs (Put: putrescine, Spd: spermidine, and Spm: spermine) at varying concentrations (0, 0.5, 1, and 1.5 mM). The subsequent outcomes were subjected to predictive modeling using diverse machine learning (ML) algorithms. Based on the specific polyamine type and concentration utilized, the results indicated that 1 mM Put and Spd were the most favorable PAs for supporting endosperm-associated mature embryos. Employing an epigenetic approach, Put at concentrations of 0.5 and 1.5 mM exhibited the highest levels of genomic template stability (GTS) (73.9%). Elevated Spd levels correlated with DNA hypermethylation while reduced Spm levels were linked to DNA hypomethylation. The in vitro and epigenetic characteristics were predicted using ML techniques such as the support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF) models. These models were employed to establish relationships between input variables (PAs, concentration, GTS rates, Msp I polymorphism, and Hpa II polymorphism) and output parameters (in vitro measurements). This comparative analysis aimed to evaluate the performance of the models and interpret the generated data. The outcomes demonstrated that the XGBoost method exhibited the highest performance scores for callus induction (CI%), regeneration efficiency (RE), and the number of plantlets (NP), with R2 scores explaining 38.3%, 73.8%, and 85.3% of the variances, respectively. Additionally, the RF algorithm explained 41.5% of the total variance and showcased superior efficacy in terms of embryogenic callus induction (ECI%). Furthermore, the SVM model, which provided the most robust statistics for responding embryogenic calluses (RECs%), yielded an R2 value of 84.1%, signifying its ability to account for a substantial portion of the total variance present in the data. In summary, this study exemplifies the application of diverse ML models to the cultivation of mature wheat embryos in the presence of various exogenous PAs and concentrations. Additionally, it explores the impact of polymorphic variations in the CRED–iPBS profile and DNA methylation on epigenetic changes, thereby contributing to a comprehensive understanding of these regulatory mechanisms.

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Section: Discussionmentioning

confidence: 99%

Investigation of the Influence of Polyamines on Mature Embryo Culture and DNA Methylation of Wheat (Triticum aestivum L.) Using the Machine Learning Algorithm Method

Eren,

Türkoğlu,

Haliloğlu

et al. 2023

Plants

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“…In the scope of this study, output variables (CI, EC, and RE) were targeted by making use of input components and molecular values, and estimate models were assessed by means of ML algorithms. The ML algorithms are very suitable for analyzing and validating projected output variables due to their ability to evaluate the input parameters associated with the desired results [ 47 , 48 , 49 , 50 ]. In recent times, there has been a growing use of machine learning models in the field of in vitro regeneration research for the purpose of data validation.…”

Section: Discussionmentioning

confidence: 99%

“…In recent times, there has been a growing use of machine learning models in the field of in vitro regeneration research for the purpose of data validation. These models have been utilized in a diverse range of studies, each with its own specific objectives and goals [ 47 , 48 , 96 ]. To the best of our knowledge, this research endeavor marks the first instance of employing ML techniques to analyze the efficacy of AgNO 3 and Ag-NPs in vitro regeneration, considering their concentrations and molecular effects as input factors.…”

Section: Discussionmentioning

confidence: 99%

“…The mean absolute deviation (MAD) is calculated by first determining the absolute difference between the actual value and the anticipated value for each data point, and then determining the average of these absolute differences. Since it uses absolute values of differences in its calculation (Equation (4)), the mean absolute deviation (MAD) measure is sensitive to the presence of substantial outliers [ 47 , 48 , 49 , 50 , 54 , 96 , 111 , 112 ].

where n is the total number of samples used for training and testing, y i is the actual value that was measured, y ip is the value that was predicted, and

is the mean of the measured values.…”

Section: Methodsmentioning

confidence: 99%

“…These methodologies often utilize variance analysis and linear regression models to determine the degree of correlation between independent input factors and dependent output variables [ 45 , 46 ]. Complex factors and non-linear features present some of the most significant challenges for researchers working in the field of plant tissue culture [ 47 , 48 ]. Addressing these challenges may be achieved through the utilization of effective high-throughput technologies, such as machine learning (ML) models.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Analysis of the Impact of Silver Nitrate and Silver Nanoparticles on Wheat (Triticum aestivum L.): Callus Induction, Plant Regeneration, and DNA Methylation

Türkoğlu,

Haliloğlu,

Demirel

et al. 2023

Plants

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The objective of this study was to comprehend the efficiency of wheat regeneration, callus induction, and DNA methylation through the application of mathematical frameworks and artificial intelligence (AI)-based models. This research aimed to explore the impact of treatments with AgNO3 and Ag-NPs on various parameters. The study specifically concentrated on analyzing RAPD profiles and modeling regeneration parameters. The treatments and molecular findings served as input variables in the modeling process. It included the use of AgNO3 and Ag-NPs at different concentrations (0, 2, 4, 6, and 8 mg L−1). The in vitro and epigenetic characteristics were analyzed using several machine learning (ML) methods, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor classifier (KNN), and Gaussian processes classifier (GP) methods. This study’s results revealed that the highest values for callus induction (CI%) and embryogenic callus induction (EC%) occurred at a concentration of 2 mg L−1 of Ag-NPs. Additionally, the regeneration efficiency (RE) parameter reached its peak at a concentration of 8 mg L−1 of AgNO3. Taking an epigenetic approach, AgNO3 at a concentration of 2 mg L−1 demonstrated the highest levels of genomic template stability (GTS), at 79.3%. There was a positive correlation seen between increased levels of AgNO3 and DNA hypermethylation. Conversely, elevated levels of Ag-NPs were associated with DNA hypomethylation. The models were used to estimate the relationships between the input elements, including treatments, concentration, GTS rates, and Msp I and Hpa II polymorphism, and the in vitro output parameters. The findings suggested that the XGBoost model exhibited superior performance scores for callus induction (CI), as evidenced by an R2 score of 51.5%, which explained the variances. Additionally, the RF model explained 71.9% of the total variance and showed superior efficacy in terms of EC%. Furthermore, the GP model, which provided the most robust statistics for RE, yielded an R2 value of 52.5%, signifying its ability to account for a substantial portion of the total variance present in the data. This study exemplifies the application of various machine learning models in the cultivation of mature wheat embryos under the influence of treatments and concentrations involving AgNO3 and Ag-NPs.

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Synergizing LED Technology and Hydropriming for Intelligent Modeling and Mathematical Expressions to Optimize Chickpea Germination and Growth Indices

Aasim,

Akin,

Ali

2024

J Plant Growth Regul

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The influence of hydropriming and Light Emitting Diodes (LED) on germination and growth indices, followed by optimizing and validation via artificial intelligence-based models was carried out in this research. White LEDs (W-LEDs) were more effective by yielding the most effective growth indices, such as mean germination time (MGT) (1.11 day), coefficient of variation of germination time (CVt) (20.72%), mean germination rate (MR) (0.81 day−1), uncertainty (U) (0.40 bit), and synchronization (Z values) (0.79); the optimum MGT (1.09 day), CVt (15.97%), MR (0.77 day−1), U (0.32 bit), and Z (0.55) values were found after 2 h of hydropriming, which was responsible for all efficient growth indicators. W-LEDs with 1 h hydropriming proved to be the ideal LED and hydropriming combination. Results on growth indices for in vitro seedlings were completely different from those on germination indices, and the most desirable germination indices were linked to red LEDs (R-LEDs). Whereas 4 h hydropriming was most effective for the post-germination process. Pareto charts, normal plots, contour plots, and surface plots were created to optimize the input variables. Finally, the data were predicted using Arificial Neural Network (ANN) inspired multilayer perceptron (MLP) and machine learning-based random forest (RF) algorithms. For both models, plant height was correlated with maximum R2 values. Whereas, all output variables had relatively low mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) scores, indicating that both models performed well. The results of this investigation disclosed a link between certain LEDs and hydropriming treatment for in vitro germination indices and plant growth. Graphical Abstract Graphical presentation of actual and predicted values for germination indices in chickpea

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Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L)

Cited by 11 publications

References 73 publications

Investigation of the Influence of Polyamines on Mature Embryo Culture and DNA Methylation of Wheat (Triticum aestivum L.) Using the Machine Learning Algorithm Method

Investigation of the Influence of Polyamines on Mature Embryo Culture and DNA Methylation of Wheat (Triticum aestivum L.) Using the Machine Learning Algorithm Method

Machine Learning Analysis of the Impact of Silver Nitrate and Silver Nanoparticles on Wheat (Triticum aestivum L.): Callus Induction, Plant Regeneration, and DNA Methylation

Synergizing LED Technology and Hydropriming for Intelligent Modeling and Mathematical Expressions to Optimize Chickpea Germination and Growth Indices

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