Forecasting and optimizing Agrobacterium-mediated genetic transformation via ensemble model- fruit fly optimization algorithm: A data mining approach using chrysanthemum databases

Abstract

Optimizing the gene transformation factors can be considered as the first and foremost step in successful genetic engineering and genome editing studies. However, it is usually difficult to achieve an optimized gene transformation protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach such as machine learning models for analyzing gene transformation data. In the current study, three individual machine learning models including Multi-Layer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Radial Basis Function (RBF) were developed for forecasting Agrobacterium-mediated gene transformation in chrysanthemum based on eleven input variables including Agrobacterium strain, optical density (OD), co-culture period (CCP), and different antibiotics including kanamycin (K), vancomycin (VA), cefotaxime (CF), hygromycin (H), carbenicillin (CA), geneticin (G), ticarcillin (TI), and paromomycin (P). Consequently, best-obtained results were used in the fusion process by bagging method. Results showed that ensemble model with the highest R 2 (0.83) had superb performance in comparison with all other individual models (MLP:063, RBF:0.69, and ANFIS: 0.74) in the validation set. Also, ensemble model was linked to Fruit fly optimization algorithm (FOA) for optimizing gene transformation, and the results showed that the maximum gene transformation efficiency (37.54%) can be achieved from EHA105 strain with 0.9 OD 600 , for 3.8 days CCP, 46.43 mg/l P, 9.54 mg/l K, 18.62 mg/l H, and 4.79 mg/l G as selection antibiotics and 109.74 μg/ml VA, 287.63 μg/ml CF, 334.07 μg/ml CA and 87.36 μg/ml TI as antibiotics in the selection medium. Moreover, sensitivity analysis demonstrated that input variables have a different degree of importance in gene transformation system in the order of Agrobacterium strain > CCP > K > CF > VA > P > OD > CA > H > TI > G. Generally, the developed hybrid model in this study (ensemble model-FOA) can be employed as an accurate and reliable approach in future genetic engineering and genome editing studies.

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

“…Recently, different machine learning algorithms have been successfully utilized to predict and optimize various plant physiological processes. Several studies [24,58,63] used MLP to predict various plant biological processes. However, they only applied the MLP model and did not compare this well-known algorithm with other models.…”

“…Hence, there is a serious need to use nonlinear statistical methodology such as artificial neural networks (ANNs). The reliability and accuracy of different ANNs such as Radial basis function (RBF), Multilayer Perceptron (MLP), and Generalized Regression Neural Network (GRNN) have been previously proven in different fields of science and technology such as in vitro culture, prediction of microRNAs and transcription factors (TFs), analysis of plant promoters, remote sensing studies, genome prediction, and phenomics studies [24][25][26][27]. ANNs are a type of nonlinear computational methods, which is applied for different aims such as clustering, predicting, and classifying the complex systems [28].…”

A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

“…Artificial intelligence (AI) is applied to address matters that cannot be clarified by traditional computational methods. Artificial neural networks (ANNs) are one of the main parts of AI discovering complex nonlinear relationships amongst input and output data [7,13,[24][25][26][27][28][29][30]. Indeed, ANNs are brain-inspired systems that emulate human brain capability of sensing and thinking, in a simplified way, to processes information and identify patterns [31].…”

Synergizing Off-Target Predictions for In Silico Insights of CENH3 Knockout in Cannabis through CRISPR/Cas

“…[ 53 ]. Therefore, ensemble algorithms were built to improve robustness over a single model with combining the predictions of several models [ 54 , 55 ]. In this study, the predictions derived from the RF, RBF, and SVM algorithms were used to build an ensemble model based on the bagging method.…”

“…F-measure is known as a reliable parameter that can be used to evaluate efficiency and accuracy of ML algorithms [ 19 ]. Recent studies have reported the success of using stochastic gradient boosting and E-B modeling in plant science [ 19 , 55 ], but not in the computational component of the plant genome editing. The E-B model exhibited the highest off-target prediction performance (AUC-ROC of 0.74 and AUC-PRC of 0.71) based on MIT and CFD scores.…”