Parameter settings. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

Dynamic resource allocation process. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

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. …”

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…In this direction, the option of estimating the statistics of the model directly from the training image (performing a refined pattern search instead of simulating data) is a very promising.<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”

MOA classification performance and model benchmarking. by Josh L. Espinoza (10492448)

“…B) The influence of the <i>Clairvoyance</i> optimization algorithm for feature selection on model performance at each of the 5 sub-model decision points. …”

Image_1_Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model.jpeg by Hao Chi (1797619)

Table_3_NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.xlsx by Yuchen Zhang (524816)

“…An information theory-based score, weighted information gain (WIG), was proposed to assess the impact of signaling genes on a specific downstream biological process of interest. …”

Table_5_NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.XLSX by Yuchen Zhang (524816)

“…An information theory-based score, weighted information gain (WIG), was proposed to assess the impact of signaling genes on a specific downstream biological process of interest. …”

Table_4_NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.xlsx by Yuchen Zhang (524816)

“…An information theory-based score, weighted information gain (WIG), was proposed to assess the impact of signaling genes on a specific downstream biological process of interest. …”

Image_1_NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.pdf by Yuchen Zhang (524816)

“…An information theory-based score, weighted information gain (WIG), was proposed to assess the impact of signaling genes on a specific downstream biological process of interest. …”

Table_1_NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.XLSX by Yuchen Zhang (524816)

“…An information theory-based score, weighted information gain (WIG), was proposed to assess the impact of signaling genes on a specific downstream biological process of interest. …”

Table_2_NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.XLSX by Yuchen Zhang (524816)

“…An information theory-based score, weighted information gain (WIG), was proposed to assess the impact of signaling genes on a specific downstream biological process of interest. …”

Image1_Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients.tif by Zhenglu Shang (11739470)

“…Moreover, four optimal feature genes were screened from the DEGs by support vector machine–recursive feature elimination (SVM-RFE) algorithm (EPB42, IFIT2, FOSB, and SNF1LK). …”

DataSheet_1_Three-Dimensional Convolutional Neural Network-Based Prediction of Epidermal Growth Factor Receptor Expression Status in Patients With Non-Small Cell Lung Cancer.docx by Xuemei Huang (170562)

DataSheet3_Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients.XLSX by Zhenglu Shang (11739470)

“…Moreover, four optimal feature genes were screened from the DEGs by support vector machine–recursive feature elimination (SVM-RFE) algorithm (EPB42, IFIT2, FOSB, and SNF1LK). …”

DataSheet2_Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients.XLSX by Zhenglu Shang (11739470)

“…Moreover, four optimal feature genes were screened from the DEGs by support vector machine–recursive feature elimination (SVM-RFE) algorithm (EPB42, IFIT2, FOSB, and SNF1LK). …”