DataSheet2_Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning.ZIP by Yuqiao Ji (16327326)

“…Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. …”

Table 1_Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma.xlsx by Duo Wang (1787634)

“…Moreover, the GPR-TME classifier as the prognosis model was constructed and further performed for immune infiltration, functional enrichment, somatic mutation, immunotherapy response prediction, and scRNA-seq analyses.…”

DataSheet1_Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning.ZIP by Yuqiao Ji (16327326)

“…Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. …”

Table1_Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning.XLS by Yuqiao Ji (16327326)

“…Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. …”

Table3_Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning.XLSX by Yuqiao Ji (16327326)

“…Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. …”

Table_7_Revealing Shared and Distinct Genes Responding to JA and SA Signaling in Arabidopsis by Meta-Analysis.XLSX by Nailou Zhang (5905592)

“…To accurately classify JA/SA analogs with as few genes as possible, 87 genes, including the SA receptor NPR4, and JA biosynthesis gene AOC1 and JA response biomarkers VSP1/2, were identified by three feature selection algorithms as JA/SA markers. The results were confirmed by independent datasets and provided valuable resources for further functional analyses in JA- or SA- mediated plant defense. …”

Image_1_Revealing Shared and Distinct Genes Responding to JA and SA Signaling in Arabidopsis by Meta-Analysis.tif by Nailou Zhang (5905592)

“…To accurately classify JA/SA analogs with as few genes as possible, 87 genes, including the SA receptor NPR4, and JA biosynthesis gene AOC1 and JA response biomarkers VSP1/2, were identified by three feature selection algorithms as JA/SA markers. The results were confirmed by independent datasets and provided valuable resources for further functional analyses in JA- or SA- mediated plant defense. …”

Table_5_Revealing Shared and Distinct Genes Responding to JA and SA Signaling in Arabidopsis by Meta-Analysis.XLS by Nailou Zhang (5905592)

“…To accurately classify JA/SA analogs with as few genes as possible, 87 genes, including the SA receptor NPR4, and JA biosynthesis gene AOC1 and JA response biomarkers VSP1/2, were identified by three feature selection algorithms as JA/SA markers. The results were confirmed by independent datasets and provided valuable resources for further functional analyses in JA- or SA- mediated plant defense. …”

Table_3_Revealing Shared and Distinct Genes Responding to JA and SA Signaling in Arabidopsis by Meta-Analysis.xls by Nailou Zhang (5905592)

“…To accurately classify JA/SA analogs with as few genes as possible, 87 genes, including the SA receptor NPR4, and JA biosynthesis gene AOC1 and JA response biomarkers VSP1/2, were identified by three feature selection algorithms as JA/SA markers. The results were confirmed by independent datasets and provided valuable resources for further functional analyses in JA- or SA- mediated plant defense. …”

Table_4_Revealing Shared and Distinct Genes Responding to JA and SA Signaling in Arabidopsis by Meta-Analysis.XLS by Nailou Zhang (5905592)

“…To accurately classify JA/SA analogs with as few genes as possible, 87 genes, including the SA receptor NPR4, and JA biosynthesis gene AOC1 and JA response biomarkers VSP1/2, were identified by three feature selection algorithms as JA/SA markers. The results were confirmed by independent datasets and provided valuable resources for further functional analyses in JA- or SA- mediated plant defense. …”

Image_2_Revealing Shared and Distinct Genes Responding to JA and SA Signaling in Arabidopsis by Meta-Analysis.TIF by Nailou Zhang (5905592)

“…To accurately classify JA/SA analogs with as few genes as possible, 87 genes, including the SA receptor NPR4, and JA biosynthesis gene AOC1 and JA response biomarkers VSP1/2, were identified by three feature selection algorithms as JA/SA markers. The results were confirmed by independent datasets and provided valuable resources for further functional analyses in JA- or SA- mediated plant defense. …”

Image_3_Revealing Shared and Distinct Genes Responding to JA and SA Signaling in Arabidopsis by Meta-Analysis.tif by Nailou Zhang (5905592)

“…To accurately classify JA/SA analogs with as few genes as possible, 87 genes, including the SA receptor NPR4, and JA biosynthesis gene AOC1 and JA response biomarkers VSP1/2, were identified by three feature selection algorithms as JA/SA markers. The results were confirmed by independent datasets and provided valuable resources for further functional analyses in JA- or SA- mediated plant defense. …”

Image_2_Revealing Shared and Distinct Genes Responding to JA and SA Signaling in Arabidopsis by Meta-Analysis.tif by Nailou Zhang (5905592)

“…To accurately classify JA/SA analogs with as few genes as possible, 87 genes, including the SA receptor NPR4, and JA biosynthesis gene AOC1 and JA response biomarkers VSP1/2, were identified by three feature selection algorithms as JA/SA markers. The results were confirmed by independent datasets and provided valuable resources for further functional analyses in JA- or SA- mediated plant defense. …”

Table_7_Revealing Shared and Distinct Genes Responding to JA and SA Signaling in Arabidopsis by Meta-Analysis.xlsx by Nailou Zhang (5905592)

“…To accurately classify JA/SA analogs with as few genes as possible, 87 genes, including the SA receptor NPR4, and JA biosynthesis gene AOC1 and JA response biomarkers VSP1/2, were identified by three feature selection algorithms as JA/SA markers. The results were confirmed by independent datasets and provided valuable resources for further functional analyses in JA- or SA- mediated plant defense. …”

DataSheet1_An Integrated Fibrosis Signature for Predicting Survival and Immunotherapy Efficacy of Patients With Hepatocellular Carcinoma.docx by Long Liu (390523)

“…Seeking a stable and novel tool to predict prognosis and immunotherapy response is imperative.</p><p>Methods: Using stepwise Cox regression, least absolute shrinkage and selection operator (LASSO), and random survival forest algorithms, the fibrosis-associated signature (FAIS) was developed and further validated. …”

New High-Pressure Phases of Titanite-Type CaTiSiO₅ Predicted from First-Principles by Subhamoy Char (12120144)

“…The present study implements a density functional theory calculation assisted structure search method using evolutionary algorithms and reveals two new structural phase transitions: displacive-type <i>monoclinic-II</i> (<i>C</i>2/<i>c</i>) → <i>triclinic</i> (<i>P</i>1̅) and reconstructive-type <i>triclinic</i> (P1̅) → <i>orthorhombic</i> (<i>Pnma</i>) at 10 and 18 GPa, respectively. …”

Table_5_Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury.XLSX by Ting Li (117885)

“…Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. …”

Table_3_Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury.XLSX by Ting Li (117885)

“…Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. …”

Image_3_Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury.pdf by Ting Li (117885)

“…Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. …”

Image2_A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients.TIF by Kemiao Yuan (15234703)

“…<p>The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. …”