Data Sheet 1_Multi-omics analysis and metastasis risk factor prediction in N1b stage PTMC: insights into immune infiltration and therapeutic implications.docx حسب Hao Dai (4277254)

Illustration of PortalCG architecture in terms of its three stages of training. حسب Tian Cai (4227052)

Data Sheet 1_Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods.docx حسب Ankita Lawarde (16544943)

"…This work highlights the power of combining miRNA network biology with ML to improve precision oncology diagnostics and supports future development of liquid biopsy-based cancer classification.…"

Table4_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls حسب Wenhao An (19366816)

"…By establishing a PPI network and employing four topological algorithms, we identified four hub genes (C1QB, CSF1R, TYROBP, and FCER1G) within the CGs, closely related to immune cell infiltration. …"

Table2_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls حسب Wenhao An (19366816)

"…By establishing a PPI network and employing four topological algorithms, we identified four hub genes (C1QB, CSF1R, TYROBP, and FCER1G) within the CGs, closely related to immune cell infiltration. …"

Table3_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls حسب Wenhao An (19366816)

"…By establishing a PPI network and employing four topological algorithms, we identified four hub genes (C1QB, CSF1R, TYROBP, and FCER1G) within the CGs, closely related to immune cell infiltration. …"

Table1_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls حسب Wenhao An (19366816)

"…By establishing a PPI network and employing four topological algorithms, we identified four hub genes (C1QB, CSF1R, TYROBP, and FCER1G) within the CGs, closely related to immune cell infiltration. …"

Table_3_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.XLSX حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"

Image_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.JPEG حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"

Image_1_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.JPEG حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"

Table_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.XLSX حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"

Breast cancer specific subnetwork. حسب Bengi Ruken Yavuz (21983004)

Table2_Integrated analysis of genes shared between type 2 diabetes mellitus and osteoporosis.XLSX حسب Fangyu Li (404719)

"…</p>Methods<p>We analyzed the GSE76894 and GSE76895 datasets for T2DM and GSE56815 and GSE7429 for OP from the Gene Expression Omnibus (GEO) database to identify shared genes in T2DM and OP, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). …"

Table1_Integrated analysis of genes shared between type 2 diabetes mellitus and osteoporosis.XLSX حسب Fangyu Li (404719)

"…</p>Methods<p>We analyzed the GSE76894 and GSE76895 datasets for T2DM and GSE56815 and GSE7429 for OP from the Gene Expression Omnibus (GEO) database to identify shared genes in T2DM and OP, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). …"

Image2_Integrated analysis of genes shared between type 2 diabetes mellitus and osteoporosis.TIF حسب Fangyu Li (404719)

"…</p>Methods<p>We analyzed the GSE76894 and GSE76895 datasets for T2DM and GSE56815 and GSE7429 for OP from the Gene Expression Omnibus (GEO) database to identify shared genes in T2DM and OP, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). …"

Image1_Integrated analysis of genes shared between type 2 diabetes mellitus and osteoporosis.TIF حسب Fangyu Li (404719)

"…</p>Methods<p>We analyzed the GSE76894 and GSE76895 datasets for T2DM and GSE56815 and GSE7429 for OP from the Gene Expression Omnibus (GEO) database to identify shared genes in T2DM and OP, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). …"

Data_Sheet_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.docx حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"

Data_Sheet_1_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.CSV حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"

DataSheet1_Integrated analysis of genes shared between type 2 diabetes mellitus and osteoporosis.ZIP حسب Fangyu Li (404719)

"…</p>Methods<p>We analyzed the GSE76894 and GSE76895 datasets for T2DM and GSE56815 and GSE7429 for OP from the Gene Expression Omnibus (GEO) database to identify shared genes in T2DM and OP, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). …"

DataSheet2_Integrated analysis of genes shared between type 2 diabetes mellitus and osteoporosis.ZIP حسب Fangyu Li (404719)

"…</p>Methods<p>We analyzed the GSE76894 and GSE76895 datasets for T2DM and GSE56815 and GSE7429 for OP from the Gene Expression Omnibus (GEO) database to identify shared genes in T2DM and OP, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). …"