Table_6_Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.csv by Nikolas Hallberg Thuesen (14072517)

Table_3_Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.csv by Nikolas Hallberg Thuesen (14072517)

Image 2_Screening key genes for intracranial aneurysm rupture using LASSO regression and the SVM-RFE algorithm.tif by Qi Wu (263110)

“…Fourteen hub genes were identified using the two algorithms. The PPI networks of the hub genes were analyzed using the Cytoscape plugin CytoNCA to obtain two key genes (IL10 and Integrin α5 (ITGA5)). …”

Table 1_Screening key genes for intracranial aneurysm rupture using LASSO regression and the SVM-RFE algorithm.xlsx by Qi Wu (263110)

“…Fourteen hub genes were identified using the two algorithms. The PPI networks of the hub genes were analyzed using the Cytoscape plugin CytoNCA to obtain two key genes (IL10 and Integrin α5 (ITGA5)). …”

Image 3_Screening key genes for intracranial aneurysm rupture using LASSO regression and the SVM-RFE algorithm.tif by Qi Wu (263110)

“…Fourteen hub genes were identified using the two algorithms. The PPI networks of the hub genes were analyzed using the Cytoscape plugin CytoNCA to obtain two key genes (IL10 and Integrin α5 (ITGA5)). …”

Image 4_Screening key genes for intracranial aneurysm rupture using LASSO regression and the SVM-RFE algorithm.tif by Qi Wu (263110)

“…Fourteen hub genes were identified using the two algorithms. The PPI networks of the hub genes were analyzed using the Cytoscape plugin CytoNCA to obtain two key genes (IL10 and Integrin α5 (ITGA5)). …”

Image 1_Screening key genes for intracranial aneurysm rupture using LASSO regression and the SVM-RFE algorithm.jpeg by Qi Wu (263110)

“…Fourteen hub genes were identified using the two algorithms. The PPI networks of the hub genes were analyzed using the Cytoscape plugin CytoNCA to obtain two key genes (IL10 and Integrin α5 (ITGA5)). …”

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

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. …”

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

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. …”

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

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. …”

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

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. …”

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

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. …”

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

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. …”

Table_2_DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis.XLS by Di Zhang (197959)

“…<p>Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. …”

Table_1_DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis.DOCX by Di Zhang (197959)

“…<p>Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. …”

Image_1_DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis.TIF by Di Zhang (197959)

“…<p>Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. …”

DataSheet1_A self-training subspace clustering algorithm based on adaptive confidence for gene expression data.PDF by Dan Li (106345)

“…<p>Gene clustering is one of the important techniques to identify co-expressed gene groups from gene expression data, which provides a powerful tool for investigating functional relationships of genes in biological process. …”

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

Rare variants in <i>SMYD1</i> and <i>BMP10</i> identified in family 346 are functionally damaging. by David M. Gordon (3259002)

Subjects:

Probably pathogenic variants (algorithms). by Felipe Fidalgo (642532)