Data Sheet 1_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by 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 2_UBE2N as a novel prognostic and therapeutic biomarker of lung adenocarcinoma.tif by 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_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Candidate prognostic genes identified from malignant epithelial subsets were further used to develop a Pyroptosis-Related Gene Signature (PRGS) through a systematic evaluation of ten machine learning algorithms and their combinations, with subsequent validation across multiple cohorts. …”

Image 3_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Candidate prognostic genes identified from malignant epithelial subsets were further used to develop a Pyroptosis-Related Gene Signature (PRGS) through a systematic evaluation of ten machine learning algorithms and their combinations, with subsequent validation across multiple cohorts. …”

Table 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…Candidate prognostic genes identified from malignant epithelial subsets were further used to develop a Pyroptosis-Related Gene Signature (PRGS) through a systematic evaluation of ten machine learning algorithms and their combinations, with subsequent validation across multiple cohorts. …”

Image 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Candidate prognostic genes identified from malignant epithelial subsets were further used to develop a Pyroptosis-Related Gene Signature (PRGS) through a systematic evaluation of ten machine learning algorithms and their combinations, with subsequent validation across multiple cohorts. …”

Image 4_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Candidate prognostic genes identified from malignant epithelial subsets were further used to develop a Pyroptosis-Related Gene Signature (PRGS) through a systematic evaluation of ten machine learning algorithms and their combinations, with subsequent validation across multiple cohorts. …”

Table 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…Candidate prognostic genes identified from malignant epithelial subsets were further used to develop a Pyroptosis-Related Gene Signature (PRGS) through a systematic evaluation of ten machine learning algorithms and their combinations, with subsequent validation across multiple cohorts. …”

Image 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Candidate prognostic genes identified from malignant epithelial subsets were further used to develop a Pyroptosis-Related Gene Signature (PRGS) through a systematic evaluation of ten machine learning algorithms and their combinations, with subsequent validation across multiple cohorts. …”

Table 2_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.xlsx by Hongxing Zhang (209372)

“…A panel of 101 machine learning algorithms was employed to screen diagnostic genes, with ROC curves used for validation. …”

Table 3_Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence.xl... by Xiaoyin Wu (15279940)

“…Single-cell RNA sequence data was used to identify cell subtypes and interactive signals. …”

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)

“…Further development of this and similar algorithms for defining and quantifying tissue- and phenotype-specificity in gene expression may allow expansion of diversity in valuable phenotypes in important crops.…”

Data Sheet 1_Identification of key genes and immune infiltration mechanisms in limb ischemia-reperfusion injury: a bioinformatics and experimental study.docx by Qiyun Shi (5531150)

“…Random forest, LASSO regression, algorithms identified feature genes, validated in a rat limb IRI model using RT-qPCR, and histology. …”

Image 1_Integrated multi-omics elucidates PRNP knockdown-mediated chemosensitization to gemcitabine in pancreatic ductal adenocarcinoma.tif by Bing Qi (492962)

“…Functional and mechanistic studies were subsequently conducted through in vitro cellular experiments.…”

Table 1_Machine learning integration with multi-omics data constructs a robust prognostic model and identifies PTGES3 as a therapeutic target for precision oncology in lung adenoca... by Lian-jie Ruan (22327876)

“…</p>Materials and methods<p>RNA-seq data from TCGA and GEO were analyzed using Cox regression and 10 machine learning algorithms to identify prognostic genes and stratify patients. …”

Data Sheet 1_Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.pdf by Lei Pu (608561)

“…A total of 101 combinations of 10 machine learning algorithms were used to construct an APIG-based prognostic signature (APIGPS). …”

Table 2_Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence.xl... by Xiaoyin Wu (15279940)

“…Single-cell RNA sequence data was used to identify cell subtypes and interactive signals. …”

Table 1_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.xlsx by Hongxing Zhang (209372)

“…A panel of 101 machine learning algorithms was employed to screen diagnostic genes, with ROC curves used for validation. …”

Image 3_Integrated multi-omics elucidates PRNP knockdown-mediated chemosensitization to gemcitabine in pancreatic ductal adenocarcinoma.tif by Bing Qi (492962)

“…Functional and mechanistic studies were subsequently conducted through in vitro cellular experiments.…”

Table 1_Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.xlsx by Lei Pu (608561)

“…A total of 101 combinations of 10 machine learning algorithms were used to construct an APIG-based prognostic signature (APIGPS). …”