Image 1_Machine learning-driven exploration of therapeutic targets for atrial fibrillation-joint analysis of single-cell and bulk transcriptomes and experimental validation.tif حسب Yicheng Wang (810922)

"…Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analyses were conducted to explore the functions and pathways of these DEGs. Three machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine—Recursive Feature Elimination (SVM-RFE), and random forest (RF), were applied to screen key genes related to AF. …"

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

"…Key diagnostic genes were screened by random forest and LASSO algorithms and validated via receiver operating characteristic (ROC) analysis. …"

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

"…Key diagnostic genes were screened by random forest and LASSO algorithms and validated via receiver operating characteristic (ROC) analysis. …"

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

"…Key diagnostic genes were screened by random forest and LASSO algorithms and validated via receiver operating characteristic (ROC) analysis. …"

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

"…Key diagnostic genes were screened by random forest and LASSO algorithms and validated via receiver operating characteristic (ROC) analysis. …"

Image 2_Machine learning-driven exploration of therapeutic targets for atrial fibrillation-joint analysis of single-cell and bulk transcriptomes and experimental validation.tif حسب Yicheng Wang (810922)

"…Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analyses were conducted to explore the functions and pathways of these DEGs. Three machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine—Recursive Feature Elimination (SVM-RFE), and random forest (RF), were applied to screen key genes related to AF. …"

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

"…Key diagnostic genes were screened by random forest and LASSO algorithms and validated via receiver operating characteristic (ROC) analysis. …"

Supplementary materials for PhD thesis - "Ontology mapping with intelligent agents on the Semantic Web : the theory and practice of agent belief and consenus building" حسب Miklos Nagy (22007560)

"…In response to these limitations, I present a multi-agent solution that uses uncertain reasoning with belief functions over similarities. …"

Image2_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.tif حسب Qingqing Long (845156)

"…A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. …"

Data Sheet 3_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv حسب Yue Pan (267516)

"…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). …"

Image1_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.tif حسب Qingqing Long (845156)

"…A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. …"

Data Sheet 1_CDK1 may promote breast cancer progression through AKT activation and immune modulation.docx حسب Huanhong Zeng (22570433)

"…Immune infiltration and tumor mutation burden (TMB) were evaluated using TIMER and CIBERSORT algorithms. Single-cell RNA sequencing data from TISCH2 were employed to examine cell-type-specific expression. …"

Data Sheet 2_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv حسب Yue Pan (267516)

"…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). …"

Image 3_UBE2N as a novel prognostic and therapeutic biomarker of lung adenocarcinoma.tif حسب Haofeng Yin (19931343)

"…The role of UBE2N in predicting tumor therapeutic susceptibility was characterized using bioinformatics algorithms combined with publicly available CRISPR screening datasets and immunotherapy cohorts. …"

Image 5_UBE2N as a novel prognostic and therapeutic biomarker of lung adenocarcinoma.tif حسب Haofeng Yin (19931343)

"…The role of UBE2N in predicting tumor therapeutic susceptibility was characterized using bioinformatics algorithms combined with publicly available CRISPR screening datasets and immunotherapy cohorts. …"

Table 1_Key gene screening and diagnostic model establishment for acute type a aortic dissection.xlsx حسب Yue Pan (267516)

"…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). …"

Image 1_UBE2N as a novel prognostic and therapeutic biomarker of lung adenocarcinoma.tif حسب Haofeng Yin (19931343)

"…The role of UBE2N in predicting tumor therapeutic susceptibility was characterized using bioinformatics algorithms combined with publicly available CRISPR screening datasets and immunotherapy cohorts. …"

Image 4_UBE2N as a novel prognostic and therapeutic biomarker of lung adenocarcinoma.tif حسب Haofeng Yin (19931343)

"…The role of UBE2N in predicting tumor therapeutic susceptibility was characterized using bioinformatics algorithms combined with publicly available CRISPR screening datasets and immunotherapy cohorts. …"

Data Sheet 4_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv حسب Yue Pan (267516)

"…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). …"

DataSheet1_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.docx حسب Qingqing Long (845156)

"…A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. …"