Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

Subjects:

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. …”

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

“…<p>The growing application of cell and gene therapies in humans leads to a need for cell type-optimized culture media. …”

Table1_Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model.xlsx by GenYi Qu (14323851)

Table2_Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model.xlsx by GenYi Qu (14323851)

Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data by Fei Xue (24567)

“…Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. …”

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. …”

Number of highest variance genes selected per omic for each dataset-approach combination. by Teemu J. Rintala (19333003)

LASSO-Cox Regression Analysis to Screen Prognosis-Related Genes. by Jie Chu (293795)

Testing algorithm for modified 2-tiered approaches to Lyme disease diagnosis. by Richard Porwancher (11413931)

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

“…Then, we applied a linear transformation and linear dimensionality reduction technique, Principal Component Analysis (PCA) to project high dimensional data to an optimal low-dimensional space. 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. …”

Data_Sheet_1_A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.PDF by Seema Khadirnaikar (9227318)

“…Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs–based classification, and identified a group of patients with high stem cell–like characteristic in the 10 most common cancer types and CCLE cell lines. …”

Data_Sheet_2_A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.xlsx by Seema Khadirnaikar (9227318)

“…Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs–based classification, and identified a group of patients with high stem cell–like characteristic in the 10 most common cancer types and CCLE cell lines. …”

DataSheet1_Dimensionality Reduction and Louvain Agglomerative Hierarchical Clustering for Cluster-Specified Frequent Biomarker Discovery in Single-Cell Sequencing Data.CSV by Soumita Seth (12052283)

“…Then, we applied a linear transformation and linear dimensionality reduction technique, Principal Component Analysis (PCA) to project high dimensional data to an optimal low-dimensional space. 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)

“…Then, we applied a linear transformation and linear dimensionality reduction technique, Principal Component Analysis (PCA) to project high dimensional data to an optimal low-dimensional space. 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)

“…Then, we applied a linear transformation and linear dimensionality reduction technique, Principal Component Analysis (PCA) to project high dimensional data to an optimal low-dimensional space. 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)

“…Then, we applied a linear transformation and linear dimensionality reduction technique, Principal Component Analysis (PCA) to project high dimensional data to an optimal low-dimensional space. 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. …”

Data Sheet 1_Clinical potential and experimental validation of prognostic genes in hepatocellular carcinoma revealed by risk modeling utilizing single cell and transcriptome constr... by Hang Deng (1664668)

“…</p>Methods<p>The HCC datasets were obtained from public databases and then differential expression analysis were used to obtain significant gene expression profiles. Subsequently, univariate Cox regression analysis and PH assumption test were performed, and a risk model was developed using an optimal algorithm from 101 combinations on the TCGA-LIHC dataset to pinpoint prognostic genes. …”

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

Table_6_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx by Ying Zhou (25031)

“…</p>Methods<p>Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”