Data_Sheet_1_Transcriptome-Based Selection and Validation of Reference Genes for Gene Expression Analysis of Alicyclobacillus acidoterrestris Under Acid Stress.PDF by Ning Zhao (84707)

“…The expression stability of eight new RGs and commonly used RG 16s rRNA was assessed using geNorm, NormFinder, and BestKeeper algorithms. Moreover, the comprehensive analysis using the RefFinder program and the validation using target gene ctsR showed that dnaG and dnaN were the optimal multiple RGs for normalization at pH 4.0; ytvI, dnaG, and 16s rRNA at pH 3.5; icd and dnaG at pH 3.0; and ytvI, dnaG, and spoVE at pH 2.5. …”

Data_Sheet_2_Transcriptome-Based Selection and Validation of Reference Genes for Gene Expression Analysis of Alicyclobacillus acidoterrestris Under Acid Stress.xls by Ning Zhao (84707)

“…The expression stability of eight new RGs and commonly used RG 16s rRNA was assessed using geNorm, NormFinder, and BestKeeper algorithms. Moreover, the comprehensive analysis using the RefFinder program and the validation using target gene ctsR showed that dnaG and dnaN were the optimal multiple RGs for normalization at pH 4.0; ytvI, dnaG, and 16s rRNA at pH 3.5; icd and dnaG at pH 3.0; and ytvI, dnaG, and spoVE at pH 2.5. …”

A secondary structure model of the effects of <i>Cis</i> mutation on splicing efficiency. by Idan Frumkin (3122832)

Fig 3 - by Ryan J. Haasl (3259872)

Table1_Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors.docx by Shuang Xu (1459348)

Table4_Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods.XLSX by ZhanDong Li (11653330)

Table3_Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods.XLSX by ZhanDong Li (11653330)

Table2_Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods.XLSX by ZhanDong Li (11653330)

Table5_Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods.XLSX by ZhanDong Li (11653330)

Table1_Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods.XLSX by ZhanDong Li (11653330)

Image1_Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients.tif by Zhenglu Shang (11739470)

“…Then 110 differentially expressed genes (DEGs) were obtained. Afterward, the PPI network based on DEGs was constructed, followed by the analysis of functional modules, whose results showed that the genes in the major function modules significantly enriched in immune-related functions. …”

Table 1_Identification of immunogenic cell death signature genes in hepatocellular carcinoma: from single-cell transcriptomics to in vitro mechanistic validation and comprehensive... by Hongliang Liu (188124)

“…We identified HCC-specific ICD-related (HCC-ICDR) genes via WGCNA and optimized a prognostic model by benchmarking machine learning algorithms. …”

DataSheet3_Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients.XLSX by Zhenglu Shang (11739470)

“…Then 110 differentially expressed genes (DEGs) were obtained. Afterward, the PPI network based on DEGs was constructed, followed by the analysis of functional modules, whose results showed that the genes in the major function modules significantly enriched in immune-related functions. …”

DataSheet2_Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients.XLSX by Zhenglu Shang (11739470)

“…Then 110 differentially expressed genes (DEGs) were obtained. Afterward, the PPI network based on DEGs was constructed, followed by the analysis of functional modules, whose results showed that the genes in the major function modules significantly enriched in immune-related functions. …”

DataSheet1_Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients.XLSX by Zhenglu Shang (11739470)

“…Then 110 differentially expressed genes (DEGs) were obtained. Afterward, the PPI network based on DEGs was constructed, followed by the analysis of functional modules, whose results showed that the genes in the major function modules significantly enriched in immune-related functions. …”

Image1_mtPCDI: a machine learning-based prognostic model for prostate cancer recurrence.pdf by Guoliang Cheng (12458973)

Table1_mtPCDI: a machine learning-based prognostic model for prostate cancer recurrence.xls by Guoliang Cheng (12458973)

Data Sheet 1_Identification of immunogenic cell death signature genes in hepatocellular carcinoma: from single-cell transcriptomics to in vitro mechanistic validation and comprehen... by Hongliang Liu (188124)

“…We identified HCC-specific ICD-related (HCC-ICDR) genes via WGCNA and optimized a prognostic model by benchmarking machine learning algorithms. …”

Entire procedures to analyze the single-cell sequencing data for virgin and pregnant mouse mammary epithelia cells. by Zhan Dong Li (12481390)

Image_7_A Prognostic Model for Acute Myeloid Leukemia Based on IL-2/STAT5 Pathway-Related Genes.tif by Yigang Tang (12031964)

“…Through Cox proportional-hazards regression model and stepwise algorithm, we constructed a six-gene STAT5-associated signature based on the most robustly survival-related genes related to the IL-2/STAT5 signaling pathway. …”