Baseline data table. by Jie Chu (293795)

Subjects:

KEGG analysis. by Jie Chu (293795)

Subjects:

Analysis process. by Jie Chu (293795)

Subjects:

Performance assessment of Cell2Spatial on real and simulated mouse brain spatial transcriptomics (ST) data. by Huamei Li (8815955)

Subjects: “…Cell Biology…”

Table 5_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.</p>Methods<p>Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. …”

Table 8_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.</p>Methods<p>Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. …”

Table 7_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.</p>Methods<p>Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. …”

Table 4_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.</p>Methods<p>Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. …”

Table 6_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.</p>Methods<p>Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. …”

Table 3_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.</p>Methods<p>Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. …”

Table 2_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.</p>Methods<p>Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. …”

Table 1_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.</p>Methods<p>Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. …”

Simulation settings of rMAPPO algorithm. by Jianbin Zheng (587000)

“…In the context of intelligent manufacturing, there is still significant potential for improving the productivity of riveting and welding tasks in existing H-beam riveting and welding work cells. In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

Data Sheet 1_Identification of novel lipid metabolism-related biomarkers of aortic dissection by integrating single-cell RNA sequencing analysis and machine learning algorithms.zip by Zhechen Li (15456929)

“…Functional characterization included cell-cell communication analysis and pseudo-time trajectory reconstruction to elucidate the roles of candidate genes in aortic dissection pathogenesis.…”

Change of ITAE objective function for en-CSA, CSA, RUN, PDO and RIME algorithms. by Sarah A. Alzakari (19704611)

Subjects:

Merged dataset with analysis of variance and visualization after removal of batch effects for GSE183272 and GSE236374. by Wenlong Kuang (20485167)

Subjects:

MitoDEGs in MI; PPI network analysis and hub MitoDEGs identification. by Wenlong Kuang (20485167)

Subjects:

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…We show that batched formation of the XC matrix from the density matrix yields the best performance for large (>O(103) basis functions), sparse systems such as glycine chains and water clusters. …”

Significant gene set results from EGSEA. by Katherine M. Peterson (19746455)

Coarse-fine optimization algorithm. by Chao Ma (207385)

“…The improved gradient extraction method combines the Scale Invariant Feature Transformation (SIFT) algorithm to form a new multi-scale image sharpness evaluation function, SIFT Quad-Tenen. …”