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Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx
Published 2025“…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …”
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Table 7_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx
Published 2025“…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …”
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Table 3_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx
Published 2025“…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …”
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Table 2_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx
Published 2025“…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …”
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Table 1_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx
Published 2025“…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …”
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Table 4_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx
Published 2025“…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …”
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Table 5_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx
Published 2025“…A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. …”
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Algorithmic experimental parameter design.
Published 2024“…This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.</p></div>…”
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Spatial spectrum estimation for three algorithms.
Published 2024“…This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.</p></div>…”
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