Light-emitting diodes induced in vitro regeneration of Alternanthera reineckii mini and validation via machine learning algorithms

Aasım, Muhammad; Ali, Seyid Amjad; Bekiş, Pınar; Nadeem, Muhammad Azhar

doi:10.1007/s11627-022-10312-6

Cited by 10 publications

(9 citation statements)

References 40 publications

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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%

“…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%

“…Addressing these challenges may be achieved through the utilization of effective high-throughput technologies, such as machine learning (ML) models. These models enable the examination and enhancement of output variables in relation to input factors [ 49 , 50 ]. The utilization of various machine learning algorithm models in the field of plant biotechnology is a growing and emerging area of research.…”

Section: Introductionmentioning

confidence: 99%

“…This methodology surpass-es traditional one-way analyses, enabling a more nuanced understanding and accurate interpretation of findings [ 53 , 54 ]. The application of machine learning has demonstrated favorable results in various areas of plant science, including in vitro germination [ 49 , 51 ], regeneration studies [ 49 , 55 ], and mutagenesis [ 56 ].…”

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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Section: Discussionmentioning

confidence: 99%

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%

Section: Introductionmentioning

confidence: 99%

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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“…This approach surpasses conventional unidirectional analyses, facilitating a nuanced comprehension and precise interpretation of results [ 24 ]. The application of ML has thus far demonstrated successful outcomes across various domains of plant science, encompassing in vitro germination [ 22 , 24 ], regeneration studies [ 22 , 25 , 26 , 27 , 28 ], in vitro mutagenesis [ 29 ], and more. In the landscape of ML algorithms, diverse models rooted in artificial intelligence principles, coupled with an array of performance metrics, are harnessed to validate predicted outcomes.…”

Section: Introductionmentioning

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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An efficient method for economic micropropagation of three aquatic plant species (Lobelia cardinalis, Staurogyne repens, and Alternanthera reineckii)

2022

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Light-emitting diodes induced in vitro regeneration of Alternanthera reineckii mini and validation via machine learning algorithms

Cited by 10 publications

References 40 publications

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

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

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

An efficient method for economic micropropagation of three aquatic plant species (Lobelia cardinalis, Staurogyne repens, and Alternanthera reineckii)

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