Table_3_Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models.docx حسب Lei Chen (54296)

Table_2_Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models.docx حسب Lei Chen (54296)

Table_1_Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models.xlsx حسب Lei Chen (54296)

Distributions of model parameters mirror differentially expressed ion channel genes. حسب Daniele Linaro (227790)

Improving Gibberellin GA₃ Production with the Construction of a Genome-Scale Metabolic Model of Fusarium fujikuroi حسب Ya-Wen Li (1733782)

الموضوعات:

Improving Gibberellin GA₃ Production with the Construction of a Genome-Scale Metabolic Model of Fusarium fujikuroi حسب Ya-Wen Li (1733782)

الموضوعات:

Improving Gibberellin GA₃ Production with the Construction of a Genome-Scale Metabolic Model of Fusarium fujikuroi حسب Ya-Wen Li (1733782)

الموضوعات:

Improving Gibberellin GA₃ Production with the Construction of a Genome-Scale Metabolic Model of Fusarium fujikuroi حسب Ya-Wen Li (1733782)

الموضوعات:

Improving Gibberellin GA₃ Production with the Construction of a Genome-Scale Metabolic Model of Fusarium fujikuroi حسب Ya-Wen Li (1733782)

الموضوعات:

primary mouse RT single cell RNA-seq حسب Mamy ANDRIANTERANAGNA (12913196)

Table1_Identification of biomarkers for hepatocellular carcinoma based on single cell sequencing and machine learning algorithms.DOCX حسب Weimin Li (131040)

"…Expression profiles of HCC cells and normal liver cells were first analyzed by maximum relevance minimum redundancy (mRMR) to get a top 50 signature gene feature. For further analysis, the incremental feature selection (IFS) method and leave-one-out cross validation (LOOCV) were conducted to build an optimal classification model and to extract 21 potentially essential biomarkers for HCC cells. …"

Table_1_One-Time Optimization of Advanced T Cell Culture Media Using a Machine Learning Pipeline.DOCX حسب Paul Grzesik (11136582)

"…When optimizing culture media for primary cells used in cell and gene therapy, traditional DoE approaches that depend on interpretable models will not always provide reliable predictions due to high donor variability. …"

A CemOrange2 marker co-localizes a mutant ABC transporter protein to the ER. حسب Brian J. Thomas (6508961)

Data_Sheet_1_A comprehensive analysis of genes associated with hypoxia and cuproptosis in pulmonary arterial hypertension using machine learning methods and immune infiltration ana... حسب Zuguang Chen (19743121)

Data_Sheet_1_Machine learning for identifying benign and malignant of thyroid tumors: A retrospective study of 2,423 patients.docx حسب Yuan-yuan Guo (13789975)

Table_1_Identification and Analysis of Glioblastoma Biomarkers Based on Single Cell Sequencing.XLSX حسب Quan Cheng (1332420)

"…Besides, an optimal classification model using a support vector machine (SVM) algorithm as the classifier was also built. …"

Image 4_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf حسب Liping Tang (77094)

"…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …"

Image 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif حسب Liping Tang (77094)

"…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …"

Image 7_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif حسب Liping Tang (77094)

"…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …"

Image 2_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf حسب Liping Tang (77094)

"…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …"