The brief description on the WTCCC dataset. by Liyan Sun (760586)

“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

The penetrance tables for the 8 DNME models. by Liyan Sun (760586)

“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

The penetrance tables for the 8 DME models. by Liyan Sun (760586)

“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

The penetrance tables for the 6 DNME3 models. by Liyan Sun (760586)

“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

Optimized combination methods for exploring novel space environment-responsive genes and their roles: insights from space-flown <i>C. elegans</i> and their implications for astrona... by Zejun Li (176523)

“…</p> <p>We employed an optimized combination algorithm that integrated two co-expression network analysis methods and four machine learning-based models to identify space environment-responsive genes (SEGs) in space-flown <i>C. elegans</i>. …”

Image_1_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.TIF... by Huangwei Wei (17011266)

“…Key genes associated with necroptosis clusters were identified using Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, and then intersected with the key gene related to AD. …”

Table_1_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.XLS... by Huangwei Wei (17011266)

“…Key genes associated with necroptosis clusters were identified using Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, and then intersected with the key gene related to AD. …”

Image_2_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.TIF... by Huangwei Wei (17011266)

“…Key genes associated with necroptosis clusters were identified using Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, and then intersected with the key gene related to AD. …”

Simultaneous Estimation of Many Sparse Networks via Hierarchical Poisson Log-Normal Model by Changhao Ge (22410399)

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. …”

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. …”

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. …”

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)

“…Last, the expression of optimal feature genes was analyzed in the GSE33463 dataset and clinical samples. …”