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). …”

Results of the PSO-Kmeans-ANMS algorithm for the Rastrigin function. by Rutinaldo Aguiar Nascimento (14259594)

Subjects:

Results of the PSO-Kmeans-ANMS algorithm for the Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

Subjects:

Results of searching performance of the improved NAG algorithm on Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

Results of searching performance of different algorithm models on the Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways. by Alexis M. Thornton (11068403)

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Results of searching performance of different algorithm models on the Sphere function and Griewank function. by Rui Zhang (13940)

Results of searching performance of the improved NAG algorithm on Sphere function and Griewank function. by Rui Zhang (13940)

Flowchart of the specific incarnation of the BO algorithm used in the experiments. by Lisa Laux (9367681)

“…To choose the next pipeline configuration to evaluate, the BO algorithm uses an Expected Improvement function to trade off maximisation of QS with the need to fully learn the GP. …”

Griewank function, Bohachevsky function and Branin’s rcos function. by Jianpeng Zhang (528185)

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Algorithm for constructing Boot-t CIs. by Qin Gong (118801)

Subjects:

AIF and VOF derived features used to feed the machine learning algorithms. by Ezequiel de la Rosa (11361108)

Illustration of methods based on Green’s function first-passage algorithms. by Jean Marc Tregan (15195173)

Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the used algorithms. by Yao Peng (1928524)

“…<p>Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the used algorithms.…”

Flowchart of DAPF-RRT algorithm. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

Performance comparison of different algorithms. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

Denoising evaluation indicators for fingerprint images using different algorithms. by Bingbing Li (461702)