Data Sheet 1_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.zip by Yunpeng Zou (3723007)

“…</p>Methods<p>We conducted differential expression and survival analyses using OS transcriptomic datasets and TCGA/GTEx data. Protein–protein interaction networks, GO/KEGG enrichment, and CytoHubba algorithms identified core hub genes. …”

Data Sheet 2_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.pdf by Yunpeng Zou (3723007)

“…</p>Methods<p>We conducted differential expression and survival analyses using OS transcriptomic datasets and TCGA/GTEx data. Protein–protein interaction networks, GO/KEGG enrichment, and CytoHubba algorithms identified core hub genes. …”

Genomics dataset of marine isolate Streptomyces griseoincarnatus strain R-35 by Danielle Dana Mitchell (20461301)

“…A total of 2570 hypothetical proteins were assigned, and 5246 proteins were assigned to function. …”

Table 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.xlsx by Liu Haoming (22524473)

“…</p>Methods<p>We analyzed TCGA-LUAD/LUSC miRNA-seq data to identify mtRNAs via mitochondrial genome alignment. Machine learning algorithms (SVM, Random Forest, Logistic Regression) classified samples using differentially expressed mtRNAs (P < 0.01, |log2FC| > 1). …”

Data Sheet 2_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv by Liu Haoming (22524473)

“…</p>Methods<p>We analyzed TCGA-LUAD/LUSC miRNA-seq data to identify mtRNAs via mitochondrial genome alignment. Machine learning algorithms (SVM, Random Forest, Logistic Regression) classified samples using differentially expressed mtRNAs (P < 0.01, |log2FC| > 1). …”

Data Sheet 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv by Liu Haoming (22524473)

“…</p>Methods<p>We analyzed TCGA-LUAD/LUSC miRNA-seq data to identify mtRNAs via mitochondrial genome alignment. Machine learning algorithms (SVM, Random Forest, Logistic Regression) classified samples using differentially expressed mtRNAs (P < 0.01, |log2FC| > 1). …”

Table2_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…Background<p>Alzheimer’s disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. …”

Table3_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…Background<p>Alzheimer’s disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. …”

Table4_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…Background<p>Alzheimer’s disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. …”

Table1_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…Background<p>Alzheimer’s disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. …”

Table6_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…Background<p>Alzheimer’s disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. …”

Table5_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…Background<p>Alzheimer’s disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. …”

Image 2_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.jpeg by Yuquan Yuan (10570747)

“…Background<p>As a type of autophagy, aggrephagy degrades the aggregation of misfolded protein in cells and plays an important role in key genetic events for various cancers. …”

Image 1_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.jpeg by Yuquan Yuan (10570747)

“…Background<p>As a type of autophagy, aggrephagy degrades the aggregation of misfolded protein in cells and plays an important role in key genetic events for various cancers. …”

Image 3_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.jpeg by Yuquan Yuan (10570747)

“…Background<p>As a type of autophagy, aggrephagy degrades the aggregation of misfolded protein in cells and plays an important role in key genetic events for various cancers. …”

Table 1_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.docx by Yuquan Yuan (10570747)

“…Background<p>As a type of autophagy, aggrephagy degrades the aggregation of misfolded protein in cells and plays an important role in key genetic events for various cancers. …”

Image 4_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.jpeg by Yuquan Yuan (10570747)

“…Background<p>As a type of autophagy, aggrephagy degrades the aggregation of misfolded protein in cells and plays an important role in key genetic events for various cancers. …”

Table 2_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.docx by Yuquan Yuan (10570747)

“…Background<p>As a type of autophagy, aggrephagy degrades the aggregation of misfolded protein in cells and plays an important role in key genetic events for various cancers. …”

MCCN Case Study 2 - Spatial projection via modelled data by Donald Hobern (21435904)

“…This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the <a href="https://pypi.org/project/PyKrige/" rel="nofollow" target="_blank">pykrige</a> and <a href="https://pypi.org/project/rioxarray/" rel="nofollow" target="_blank">rioxarray</a> packages.…”

Data Sheet 1_Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.pdf by Guangzong Li (16696443)

“…Eleven ML algorithms were trained and validated using Python, and external validation and performance evaluations were conducted. …”