DataSheet_1_Trans-population graph-based coverage optimization of allogeneic cellular therapy.xlsx by Sapir Israeli (15415241)

“…We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing.</p>Study design<p>We developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). …”

DataSheet_1_Trans-population graph-based coverage optimization of allogeneic cellular therapy.xlsx by Sapir Israeli (15415241)

“…We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing.</p>Study design<p>We developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). …”

DataSheet_1_Trans-population graph-based coverage optimization of allogeneic cellular therapy.xlsx by Sapir Israeli (15415241)

“…We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing.</p>Study design<p>We developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). …”

Table1_Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.docx by Haohan Xue (17892128)

“…The single, double, and triple kernel functions were RBF kernel function, the integration of RBF and polynomial kernel functions, and the integration of RBF, polynomial, and linear kernel functions respectively. The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. …”

DataSheet1_Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.ZIP by Haohan Xue (17892128)

“…The single, double, and triple kernel functions were RBF kernel function, the integration of RBF and polynomial kernel functions, and the integration of RBF, polynomial, and linear kernel functions respectively. The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. …”

DataSheet1_Dimensionality Reduction and Louvain Agglomerative Hierarchical Clustering for Cluster-Specified Frequent Biomarker Discovery in Single-Cell Sequencing Data.CSV by 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. …”

DataSheet4_Dimensionality Reduction and Louvain Agglomerative Hierarchical Clustering for Cluster-Specified Frequent Biomarker Discovery in Single-Cell Sequencing Data.CSV by 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 by 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 by 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 by 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 by 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 by 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 by 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 by Zhang Haolong (17456619)

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

Sample characteristics. by 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. by 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. by 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. …”

Table_1_Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation.xlsx by 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.…”

Maternal blood <i>EBF1</i>-based microRNA transcripts as biomarkers for detecting risk of spontaneous preterm birth: a nested case-control study by 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. …”

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