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). …”

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. …”

Data Sheet 1_Unsupervised method for representation transfer from one brain to another.docx by Daiki Nakamura (20349885)

“…<p>Although the anatomical arrangement of brain regions and the functional structures within them are similar across individuals, the representation of neural information, such as recorded brain activity, varies among individuals owing to various factors. …”

Table 3_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx by Jian Wang (5901)

“…Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

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)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

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)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

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)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

Table 1_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx by Jian Wang (5901)

“…Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

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)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

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)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

Image 1_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.tif by Jian Wang (5901)

“…Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

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)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

Table 2_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx by Jian Wang (5901)

“…Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

“…Differentially altered pathways were evaluated by using the enrich plot package in R for visualization of functional enrichment (i.e., dot plot).</p>…”

A Smoothed-Bayesian Approach to Frequency Recovery from Sketched Data by Mario Beraha (11669142)

“…For sketches obtained with a single hash function, our approach is supported by precise theoretical guarantees, including unbiasedness and optimality under a Bayesian framework within an intuitive class of linear estimators. …”

Table 1_CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines.xlsx by Shubham Choudhury (9192026)

“…<p>The function of long non-coding RNA (lncRNA) is largely determined by its specific location within a cell. …”

Bioinformatics-based screening and experimental validation of biomarkers for the treatment of connective tissue-associated interstitial lung disease with liquorice and dried ginger... by Hui Yuan (402180)

“…</p> <p>Public datasets of Peripheral blood mononuclear cells (PBMCs) from CTD-ILD (n = 4) and connective tissue disease-associated non-Inflammatory lung disease (CTD-NILD) (n = 3) patients were analyzed using differential expression (p.adj < 0.05 & |log2 Fold Change (FC)| > 0.5), protein-protein interaction networks, and cytohubba algorithms (Top5 genes from six algorithms). …”

Table 1_Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning.docx by Tao Zhou (117050)

“…Hub genes in IC/BPS patients were identified through the application of three distinct machine-learning algorithms. Additionally, the inflammatory status and immune landscape of IC/BPS patients were evaluated using the ssGSEA algorithm. …”