Image_4_Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.TIF by Xingchen Liu (524190)

“…The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. …”

Table_1_Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.XLSX by Xingchen Liu (524190)

“…The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. …”

Image_11_Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.TIF by Xingchen Liu (524190)

“…The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. …”

Image_13_Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.TIF by Xingchen Liu (524190)

“…The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. …”

Image_2_Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.TIF by Xingchen Liu (524190)

“…The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. …”

Image_7_Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.TIF by Xingchen Liu (524190)

“…The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. …”

Image_8_Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.TIF by Xingchen Liu (524190)

“…The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. …”

Image_12_Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.TIF by Xingchen Liu (524190)

“…The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. …”

Image_14_Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.TIF by Xingchen Liu (524190)

“…The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. …”

Image_15_Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.TIF by Xingchen Liu (524190)

“…The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. …”

Image_10_Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.TIF by Xingchen Liu (524190)

“…The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. …”

Table_1_Identification of diagnostic biomarkers in Alzheimer’s disease by integrated bioinformatic analysis and machine learning strategies.xlsx by Boru Jin (14245421)

“…Four models were employed to screen the potential diagnostic biomarkers, including one bioinformatic analysis of Weighted gene co-expression network analysis (WGCNA)and three machine-learning algorithms: Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) analysis. …”

Data_Sheet_1_Identification of diagnostic biomarkers in Alzheimer’s disease by integrated bioinformatic analysis and machine learning strategies.PDF by Boru Jin (14245421)

“…Four models were employed to screen the potential diagnostic biomarkers, including one bioinformatic analysis of Weighted gene co-expression network analysis (WGCNA)and three machine-learning algorithms: Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) analysis. …”

Data Sheet 3_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). …”

Data Sheet 2_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). …”

Table 1_Identification and validation of CKAP2 as a novel biomarker in the development and progression of rheumatoid arthritis.docx by Qiongbing Zheng (4741563)

“…Differentially expressed gene (DEG) analysis, functional enrichment analysis, and weighted gene co-expression network analysis (WGCNA) identified key gene modules in RA. …”

Table1_“Dictionary of immune responses” reveals the critical role of monocytes and the core target IRF7 in intervertebral disc degeneration.xls by Peichuan Xu (19862055)

“…Differential gene expression analysis, PPI network, GO and KEGG pathway enrichment analysis, GSVA, co-expressed gene analysis and key gene-related networks were also performed to explore hub genes and their associated functions. …”

Table 1_Key gene screening and diagnostic model establishment for acute type a aortic dissection.xlsx by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). …”

Image2_“Dictionary of immune responses” reveals the critical role of monocytes and the core target IRF7 in intervertebral disc degeneration.jpeg by Peichuan Xu (19862055)

“…Differential gene expression analysis, PPI network, GO and KEGG pathway enrichment analysis, GSVA, co-expressed gene analysis and key gene-related networks were also performed to explore hub genes and their associated functions. …”

Data Sheet 4_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). …”