Data Sheet 1_Functional partitioning through competitive learning.pdf by Marius Tacke (22563344)

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). …”

Riemann Hypothesis Solution: A Structured Hilbert-Polya Operator Realization via Entropy Geometry by Jennifer Bulyaki (22283308)

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. …”

Observed sample means and standard deviations of the distributions of the % ARI in the 50 simulations; comparing analyses where the alternative methods were performed with either <... by Susana Conde (5495675)

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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Data for "A multi-objective platform for autonomous property targeting and optimization of colloidal lead halide perovskite quantum dots" by Neal Munyebvu (19690954)

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

Comparative experimental ranking results of BWEMFO and other famous optimization methods on IEEE CEC 2017 test functions. by Jingjing Ma (419752)

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