Table1_A real-world pharmacovigilance analysis of eslicarbazepine acetate using the FDA adverse events reporting system (FAERS) database from 2013 (Q4) to 2024 (Q1).XLSX by Huafei Tang (19710232)

“…</p>Methods<p>By extracting all available data since the FDA approval of ESL (2013Q4-2024Q1), disproportionality analysis was performed using reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN) and multi-item gamma Poisson shrinker (MGPS) algorithms. …”

Table2_A real-world pharmacovigilance analysis of eslicarbazepine acetate using the FDA adverse events reporting system (FAERS) database from 2013 (Q4) to 2024 (Q1).XLSX by Huafei Tang (19710232)

“…</p>Methods<p>By extracting all available data since the FDA approval of ESL (2013Q4-2024Q1), disproportionality analysis was performed using reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN) and multi-item gamma Poisson shrinker (MGPS) algorithms. …”

Polyanion sodium cathode materials dataset by Martin Hoffmann Petersen (13626778)

“…An example of how to extract data and plot the physical properties is provided in https://github.com/dtu-energy/cathode-generation-workflow/tree/main/extract_data/read_data.py and https://github.com/dtu-energy/cathode-generation-workflow/tree/main/extract_data/utils.py contains two functions, one used to attached Bader charges to an ASE atom object an another to combine multiple XYZ data files.…”

Table 1_Development of an alkaliptosis-related lncRNA risk model and immunotherapy target analysis in lung adenocarcinoma.docx by Xiang Xiong (1810234)

“…</p>Methods<p>The TCGA database was utilized to obtain RNA-seq-based transcriptome profiling data, clinical information, and mutation data. We conducted multivariate Cox regression analysis to identify alkaliptosis-related lncRNAs. …”

Methodology block diagram. by Gahao Chen (21688843)

“…Current machine learning (ML) models demonstrate suboptimal predictive performance in KD treatment response prediction, primarily due to their limited ability to effectively process categorical variables and interpret tabular clinical data. …”

Figures and Tables by Divya C D (22799186)

“…Robots Comput. Vision XXXI: Algorithms and Techniques, Burlingame, CA, USA, Jan. 23–24, 2012.…”

CANDID-II Dataset by Sijing Feng (10187216)

“…This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.</p><p dir="ltr">Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID II dataset is to be accessed by the external users. …”

Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Image 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Image 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Image 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Image 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 2_Artificial intelligence in vaccine research and development: an umbrella review.docx by Rabie Adel El Arab (14113425)

“…Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, and ethical concerns over transparency and equity.…”

Table 3_Artificial intelligence in vaccine research and development: an umbrella review.docx by Rabie Adel El Arab (14113425)

“…Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, and ethical concerns over transparency and equity.…”

Table 1_Artificial intelligence in vaccine research and development: an umbrella review.docx by Rabie Adel El Arab (14113425)

“…Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, and ethical concerns over transparency and equity.…”

CANDID-III Dataset by Sijing Feng (10187216)

“…This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.</p><p dir="ltr">Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID III dataset is to be accessed by the external users. …”

Table 2_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx by Rabie Adel El Arab (14113425)

“…</p>Ethical implications<p>Simultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.…”