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. …”

Table 2_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 8_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. …”

Table 6_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. …”

Table 9_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 5_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 11_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.csv 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 7_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. …”

Image 3_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 10_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.csv 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_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. …”

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. …”

Forecasting most deleterious nsSNPs in human <i>TLR9</i> gene and their cumulative impact on biophysical features of the protein using <i>in silico</i> approaches by Heena Gautam (14419563)

“…<p>In women, the uterine cervix and corpus uteri are two main suspects, playing a major role in cancer-associated-mortality. Immunologically, Toll-like receptors (TLRs) associated with the innate immune system, can recognize pathogens and induce immune responses against pathogens. …”

Image 4_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.tif by Zhi-Chuan He (21563657)

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”

Image 3_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.tif by Zhi-Chuan He (21563657)

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”