Controlling FDR with multiple different <i>α</i> values. by Aapo E. Korhonen (22530674)

Enrichment analysis results for COPDGene data with respect to . by Weixuan Liu (19137115)

Enrichment analysis results for TCGA breast cancer data with respect to tumor purity. by Weixuan Liu (19137115)

Table 2_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. Integration of cell-free DNA (cfDNA) methylation markers yielded a multi-omics model with slightly enhanced performance (AUROC=0.98), but the gain over the metabolomics-only model was modest.…”

Table 8_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. Integration of cell-free DNA (cfDNA) methylation markers yielded a multi-omics model with slightly enhanced performance (AUROC=0.98), but the gain over the metabolomics-only model was modest.…”

Table 1_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. Integration of cell-free DNA (cfDNA) methylation markers yielded a multi-omics model with slightly enhanced performance (AUROC=0.98), but the gain over the metabolomics-only model was modest.…”

Table 4_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. Integration of cell-free DNA (cfDNA) methylation markers yielded a multi-omics model with slightly enhanced performance (AUROC=0.98), but the gain over the metabolomics-only model was modest.…”

Table 5_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. Integration of cell-free DNA (cfDNA) methylation markers yielded a multi-omics model with slightly enhanced performance (AUROC=0.98), but the gain over the metabolomics-only model was modest.…”

Table 6_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. Integration of cell-free DNA (cfDNA) methylation markers yielded a multi-omics model with slightly enhanced performance (AUROC=0.98), but the gain over the metabolomics-only model was modest.…”

Table 7_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. Integration of cell-free DNA (cfDNA) methylation markers yielded a multi-omics model with slightly enhanced performance (AUROC=0.98), but the gain over the metabolomics-only model was modest.…”

Table 3_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. Integration of cell-free DNA (cfDNA) methylation markers yielded a multi-omics model with slightly enhanced performance (AUROC=0.98), but the gain over the metabolomics-only model was modest.…”

Data Sheet 1_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.docx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. Integration of cell-free DNA (cfDNA) methylation markers yielded a multi-omics model with slightly enhanced performance (AUROC=0.98), but the gain over the metabolomics-only model was modest.…”

Image 1_Single-cell and multi-omics analysis reveals the role of stem cells in prognosis and immunotherapy of lung adenocarcinoma patients.tif by Jianan Zheng (9134099)

Image 2_Single-cell and multi-omics analysis reveals the role of stem cells in prognosis and immunotherapy of lung adenocarcinoma patients.tif by Jianan Zheng (9134099)

Data Sheet 4_Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.pdf by Zheng Ye (15102)

“…Background<p>Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. …”

Data Sheet 6_Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.pdf by Zheng Ye (15102)

“…Background<p>Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. …”

Data Sheet 11_Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.pdf by Zheng Ye (15102)

“…Background<p>Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. …”

Data Sheet 7_Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.pdf by Zheng Ye (15102)

“…Background<p>Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. …”

Data Sheet 9_Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.pdf by Zheng Ye (15102)

“…Background<p>Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. …”

Data Sheet 1_Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.pdf by Zheng Ye (15102)

“…Background<p>Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. …”