Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Data Sheet 1_Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine lear... by Qianqian Liang (1521904)

“…</p>Results<p>Cross-analysis identified 186 shared DEGs between diabetes and COPD patients. Functional enrichment results demonstrate that metabolic and immune-related pathways are common features altered in both diabetes and COPD patients. …”

Data Sheet 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.csv by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Data Sheet 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.csv by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 2_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Image 3_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Image 1_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

<b>NanoNeuroBot: Beyond Healing, Toward Human Connection</b> by ahmed hossam (21420446)

“…The research also explores development timelines, projected budgets, ethical concerns, and translational potential in both clinical and commercial applications.</p>…”

Image 4_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Table 1_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.docx by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Image 5_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Table_1_Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning.docx by Wenhui Chen (3208248)

“…By analyzing differentially expressed genes (DEGs) and module genes using weighted gene co-expression networks (WGCNA), functional enrichment analysis, and three machine learning algorithms, we identified twelve diseases shared genes, and two diagnostic genes, including GLIPR1 and MAMLD1. …”

Image 2_Integrated transcriptomic and single-cell RNA-seq analysis identifies CLCNKB, KLK1 and PLEKHA4 as key gene of AKI-to-CKD progression.tif by Fanhua Zeng (2097133)

“…Functional enrichment, drug prediction analyses and immune cells infiltration were conducted to investigate the functional mechanisms of the identified biomarkers. …”