Lasso gene RF hub gene. by Yinghao Ren (17915291)

“…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”

Enrichment analysis of KEGG. by Yinghao Ren (17915291)

“…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”

Image 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

“…Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”

Table 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

“…Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”

Image 2_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

“…Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”

Table 1_Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.docx by Chen Lin (95910)

“…Finally, we used functional enrichment analysis to identify potential therapeutic agents for AKI.…”

Table 3_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 5_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 8_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 6_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 4_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Image 5_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.jpeg by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 9_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 7_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Image 3_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.jpeg by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Image 4_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.jpeg by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

DataSheet1_Identification of potential auxin response candidate genes for soybean rapid canopy coverage through comparative evolution and expression analysis.zip by Deisiany Ferreira Neres (10484791)

“…Many strategies have been developed to improve both genetic and trait diversity in crops, from backcrossing with wild relatives, to chemical/radiation mutagenesis, to genetic engineering. …”

Supplementary file 1_Comprehensive analysis of phagocytosis regulatory genes in bladder cancer: implications for prognosis and immunotherapy.xlsx by Xueming Ma (2119150)

“…</p>Methods<p>Multi-omics data from the TCGA and GEO databases were integrated, and strict data preprocessing was carried out. A variety of algorithms and analysis techniques, such as Kaplan-Meier analysis, Cox regression analysis, and ConsensusClusterPlus clustering analysis, were used to identify PRGs related to the prognosis of bladder cancer patients, and functional analysis and clustering analysis were conducted in depth. …”

Supplementary file 1_Socioeconomic status and lifestyle as factors of multimorbidity among older adults in China: results from the China Health and Retirement Longitudinal Survey.d... by Wei Gong (112494)

“…</p>Results<p>XGBoost achieved the best predictive performance (AUC = 0.788 on the test set), outperforming both linear and non-linear models across most evaluation metrics. …”

Table 1_Identification of crosstalk genes and diagnostic biomarkers in systemic sclerosis associated sarcopenia through integrative analysis and machine learning.docx by Yanfang Wu (206076)

“…PCR validation confirmed the differential expression of NOX4 and NEK6 in both SSc and SSc-associated sarcopenia, demonstrating high predictive accuracy. …”