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. …”

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. …”

Image 2_Spatial transcriptomic analysis of 4NQO-induced tongue cancer revealed cellular lineage diversity and evolutionary trajectory.tif by Feng Liu (72874)

“…</p>Methods<p>We leveraged artificial intelligence (AI) algorithms and spatial transcriptomic sequencing to meticulously characterize the spatial and temporal evolution of 4-nitroquinoline-1-oxide (4NQO)-induced tongue carcinogenesis and intratumor heterogeneity.…”

Image 1_Spatial transcriptomic analysis of 4NQO-induced tongue cancer revealed cellular lineage diversity and evolutionary trajectory.tif by Feng Liu (72874)

“…</p>Methods<p>We leveraged artificial intelligence (AI) algorithms and spatial transcriptomic sequencing to meticulously characterize the spatial and temporal evolution of 4-nitroquinoline-1-oxide (4NQO)-induced tongue carcinogenesis and intratumor heterogeneity.…”

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 3_Spatial transcriptomic analysis of 4NQO-induced tongue cancer revealed cellular lineage diversity and evolutionary trajectory.tif by Feng Liu (72874)

“…</p>Methods<p>We leveraged artificial intelligence (AI) algorithms and spatial transcriptomic sequencing to meticulously characterize the spatial and temporal evolution of 4-nitroquinoline-1-oxide (4NQO)-induced tongue carcinogenesis and intratumor heterogeneity.…”

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. …”

Image 1_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. …”

Table 1_Integrated transcriptomic and single-cell RNA-seq analysis identifies CLCNKB, KLK1 and PLEKHA4 as key gene of AKI-to-CKD progression.xlsx by Fanhua Zeng (2097133)

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

Image 4_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.jpeg by Jiafeng Peng (22116253)

“…Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. …”

Table 4_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.xlsx by Jiafeng Peng (22116253)

“…Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. …”

Image 2_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.tif by Jiafeng Peng (22116253)

“…Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. …”

Table 5_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.xlsx by Jiafeng Peng (22116253)

“…Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. …”

Image 1_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.tif by Jiafeng Peng (22116253)

“…Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. …”

Table 3_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.docx by Jiafeng Peng (22116253)

“…Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. …”

Table 7_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.xlsx by Jiafeng Peng (22116253)

“…Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. …”

Image 6_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.jpeg by Jiafeng Peng (22116253)

“…Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. …”

Table 1_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.xlsx by Jiafeng Peng (22116253)

“…Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. …”