WGCNA and machine learning algorithms. حسب Shushu Wang (6870191)

Key gene identification based on two machine learning algorithms. حسب Lijun Guo (605276)

Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation. حسب Changze Ou (22806374)

Construction of the PRG score index using integrated machine learning algorithms. حسب Xiangxin Wu (22320058)

الموضوعات:

Model construction and validation (A) Construction of the model based on multiple machine learning algorithms. حسب Chengting Wu (21485612)

Machine learning analysis of 10 hub genes related to SCI was performed using eight algorithms applied to the dataset. حسب Tianbao Feng (21722233)

Table 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.xlsx حسب 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 حسب 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 حسب 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 حسب 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 حسب 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 حسب 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). …"

Functional analysis of anoikis-related genes. حسب Yufeng He (5673119)

الموضوعات:

Cell-type marker genes and functional enrichment analysis. حسب Hailin Li (453832)

الموضوعات:

Multimodal reference functions. حسب Ruiyu Zhan (21602031)

"…Notably, the F1-scores, which balance precision and recall, were 0.69 for miRNA expression, 0.65 for gene expression, and 0.62 for DNA methylation. …"

(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. حسب Devi Soorya Narayana Sasikumar (22331743)

الموضوعات:

Performance of ENQUIRE’s gene normalization algorithm. حسب Luca Musella (19986312)

Functional enrichment analyses of Co-genes. حسب Qingcong Zheng (11169094)

The convergence curves of the test functions. حسب Ruiyu Zhan (21602031)

"…Notably, the F1-scores, which balance precision and recall, were 0.69 for miRNA expression, 0.65 for gene expression, and 0.62 for DNA methylation. …"

Single-peaked reference functions. حسب Ruiyu Zhan (21602031)

"…Notably, the F1-scores, which balance precision and recall, were 0.69 for miRNA expression, 0.65 for gene expression, and 0.62 for DNA methylation. …"