10-fold cross-validation evaluation of accuracy of animal tracking algorithms. by Catalin Mitelut (5587493)

Table 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.xlsx by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

Image 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

Image 4_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

Image 5_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

Image 3_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

Image 2_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

The structure of genetic algorithm (GA). by Ali Akbar Moosavi (17769033)

“…First, physico-chemical inputs as bulk density (BD), initial water content (W_i), saturated water content (W_s), mean weight diameter (MWD), and geometric mean diameter (GMD) of aggregates, pH, electrical conductivity (EC), and calcium carbonate equivalent (CCE) were measured. Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

Radar chart of classification accuracy of IRBMO vs. other algorithms. by Chenyi Zhu (9383370)

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Number of features selected for IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

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Fitness values of IRBMO vs. other feature selection algorithm. by Chenyi Zhu (9383370)

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Classification accuracy values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

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F-score values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

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Iterative graph of IRBMO vs. other feature selection algorithms. by Chenyi Zhu (9383370)

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Optimization Performance Comparison of Metaheuristic Algorithms. by Xie Qiu (13243770)

Anthropometric and functional measurements of the study participants. by Naiara Virto (20490408)

Comparative performance evaluation of 3Mont with other feature selection algorithms over 10 iterations. by Miray Unlu Yazici (14792680)

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Table 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 4_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 5_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”