Critical values of membership functions learned from the study cohort by the algorithm. by Yufeng Zhang (195768)

List of feature selection methods used in the voting algorithm. by Mahmoud Zeydabadinezhad (12289570)

Subjects:

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”

Convergence results of different dimensional functions under different algorithms. by Guilian Feng (18530806)

Standard functions to test. by Kemal Gökhan Nalbant (18858009)

Search Algorithms and Loss Functions for Bayesian Clustering by David B. Dahl (11761055)

“…<p>We propose a randomized greedy search algorithm to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. …”

Benchmark functions. by Mingjun Li (178925)

Subjects:

Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>14−<i>f</i>23. by Ying Li (38224)

Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>8−<i>f</i>13. by Ying Li (38224)

The eVIP algorithm uses RNA-seq data to predict the function of somatic mutations. by Alexis M. Thornton (11068403)

Subjects:

The mAP for all algorithms and 80% of the data used for training. by Mohammad Amin Fraiwan (20469541)

The mAP for all algorithms and 50% of the data used for training. by Mohammad Amin Fraiwan (20469541)

The mAP for all algorithms and 60% of the data used for training. by Mohammad Amin Fraiwan (20469541)

Three-dimensional function image for testing algorithm performance. by Jianpeng Zhang (528185)

Subjects:

Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

Subjects:

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). …”