DataSheet4_Dimensionality Reduction and Louvain Agglomerative Hierarchical Clustering for Cluster-Specified Frequent Biomarker Discovery in Single-Cell Sequencing Data.CSV حسب Soumita Seth (12052283)

"…After identifying fifty “significant”principal components (PCs) based on strong enrichment of low p-value features, we implemented a graph-based clustering algorithm Louvain for the cell clustering of 10 top significant PCs. …"

DataSheet2_Dimensionality Reduction and Louvain Agglomerative Hierarchical Clustering for Cluster-Specified Frequent Biomarker Discovery in Single-Cell Sequencing Data.CSV حسب Soumita Seth (12052283)

"…After identifying fifty “significant”principal components (PCs) based on strong enrichment of low p-value features, we implemented a graph-based clustering algorithm Louvain for the cell clustering of 10 top significant PCs. …"

Image1_Dimensionality Reduction and Louvain Agglomerative Hierarchical Clustering for Cluster-Specified Frequent Biomarker Discovery in Single-Cell Sequencing Data.JPEG حسب Soumita Seth (12052283)

"…After identifying fifty “significant”principal components (PCs) based on strong enrichment of low p-value features, we implemented a graph-based clustering algorithm Louvain for the cell clustering of 10 top significant PCs. …"

DataSheet3_Dimensionality Reduction and Louvain Agglomerative Hierarchical Clustering for Cluster-Specified Frequent Biomarker Discovery in Single-Cell Sequencing Data.CSV حسب Soumita Seth (12052283)

"…After identifying fifty “significant”principal components (PCs) based on strong enrichment of low p-value features, we implemented a graph-based clustering algorithm Louvain for the cell clustering of 10 top significant PCs. …"

Table1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx حسب Haolong Zhang (13911013)

"…The optimal model was determined based on the AUC values derived from various algorithms. …"

Table1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx حسب Zhang Haolong (17456619)

"…The optimal model was determined based on the AUC values derived from various algorithms. …"

DataSheet1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx حسب Haolong Zhang (13911013)

"…The optimal model was determined based on the AUC values derived from various algorithms. …"

DataSheet1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx حسب Zhang Haolong (17456619)

"…The optimal model was determined based on the AUC values derived from various algorithms. …"

Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP حسب Xiaoyuan Wang (492534)

"…In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. …"

Sample characteristics. حسب Paul Schmidt-Barbo (18954994)

"…We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphoma samples—nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL)—alongside with 291 control samples. …"

Numbers of BCR repertoires used for training. حسب Paul Schmidt-Barbo (18954994)

"…We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphoma samples—nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL)—alongside with 291 control samples. …"

Comprehensive table of data samples. حسب Paul Schmidt-Barbo (18954994)

"…We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphoma samples—nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL)—alongside with 291 control samples. …"

Maternal blood <i>EBF1</i>-based microRNA transcripts as biomarkers for detecting risk of spontaneous preterm birth: a nested case-control study حسب Guoli Zhou (225819)

"…Receiver operating characteristic (ROC) analyses were used to identify the maximum Youden Index and its corresponding optimal sensitivity/specificity cut-point of <i>EBF1</i>-based miRNA transcripts for classifying sPTB, and to compare the classification performance of a linear combination (score) of miRNA transcripts with that of individual miRNA transcripts. …"

Table_1_Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation.xlsx حسب Joanna Tobiasz (11816504)

"…We applied a dynamic programming (DP) algorithm to create a piecewise (segmented) multivariate linear regression model predicting SF2 and to identify SF2 segment-related distinctive CNVs.…"

Medium-throughput filtering by structural modeling and microarray screening. حسب Jérôme Tubiana (14559981)

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. حسب Enrico Bertozzi (22461709)

"…Model evaluation was based on accuracy metrics and qualitative analysis of the confusion matrix.. …"

Image_2_Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer.tif حسب Xin Qing (10730751)

"…The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. …"

Image_1_Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer.tif حسب Xin Qing (10730751)

"…The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. …"

DataSheet_1_Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer.zip حسب Xin Qing (10730751)

"…The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. …"

Bayesian sequential design for sensitivity experiments with hybrid responses حسب Yuxia Liu (1779592)

"…<p>In experimental design, a common problem seen in practice is when the result includes one binary response and multiple continuous responses. …"