Image 1_Multi-omics integration analysis based on plasma circulating proteins reveals potential therapeutic targets for ulcerative colitis.pdf by Jihai Zhou (1876561)

“…This study aims to identify potential diagnostic and therapeutic biomarkers for UC through multi-omics integrative analysis, providing new insights into its precise diagnosis and treatment.</p>Methods<p>Data samples from the Gene Expression Omnibus database and protein quantitative trait loci data from genome-wide association studies were integrated to identify overlapping genes. …”

Table 1_Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation.docx by Hong Chen (108084)

“…Clinical, demographic, and laboratory data were processed with rigorous quality control. …”

Table 2_Identification and validation of pyroptosis-related genes in Alzheimer’s disease based on multi-transcriptome and machine learning.docx by Yuntai Wang (21356525)

“…Additionally, we validated the expression patterns of these key genes using the expression data from AD mice and constructed potential regulatory networks through time series and correlation analysis.…”

Table 1_Identification and validation of pyroptosis-related genes in Alzheimer’s disease based on multi-transcriptome and machine learning.xlsx by Yuntai Wang (21356525)

“…Additionally, we validated the expression patterns of these key genes using the expression data from AD mice and constructed potential regulatory networks through time series and correlation analysis.…”

Image 4_The evolution of artificial intelligence technology in non-alcoholic fatty liver disease.jpeg by Jiawen He (7828505)

“…Recent research trends indicate that deep learning algorithms and multimodal data fusion have become research hotspots in AI applications for NAFLD diagnosis and treatment. …”

Table 1_The evolution of artificial intelligence technology in non-alcoholic fatty liver disease.docx by Jiawen He (7828505)

“…Recent research trends indicate that deep learning algorithms and multimodal data fusion have become research hotspots in AI applications for NAFLD diagnosis and treatment. …”

Image 3_The evolution of artificial intelligence technology in non-alcoholic fatty liver disease.jpeg by Jiawen He (7828505)

“…Recent research trends indicate that deep learning algorithms and multimodal data fusion have become research hotspots in AI applications for NAFLD diagnosis and treatment. …”

Image 1_The evolution of artificial intelligence technology in non-alcoholic fatty liver disease.jpeg by Jiawen He (7828505)

“…Recent research trends indicate that deep learning algorithms and multimodal data fusion have become research hotspots in AI applications for NAFLD diagnosis and treatment. …”

Table 2_The evolution of artificial intelligence technology in non-alcoholic fatty liver disease.docx by Jiawen He (7828505)

“…Recent research trends indicate that deep learning algorithms and multimodal data fusion have become research hotspots in AI applications for NAFLD diagnosis and treatment. …”

Image 2_The evolution of artificial intelligence technology in non-alcoholic fatty liver disease.jpeg by Jiawen He (7828505)

“…Recent research trends indicate that deep learning algorithms and multimodal data fusion have become research hotspots in AI applications for NAFLD diagnosis and treatment. …”

Preprocessed GEFCom 2012 Wind Power Forecasting Dataset by Yuan Shi (22779782)

“…</p><p dir="ltr">This processed dataset is suitable for research and development in wind power forecasting models, offering a clean and standardized input for machine learning algorithms.…”

Supplementary file 2_Educators’ reflections on AI-automated feedback in higher education: a structured integrative review of potentials, pitfalls, and ethical dimensions.docx by Latifah Hamdan Alghamdi (22611518)

“…Research indicates that AI can enhance customization, deliver immediate feedback, optimize repetitive processes, and increase student engagement. Nonetheless, these advantages are persistently compromised by concerns regarding algorithmic bias, data privacy, the deterioration of teacher-student relationships, and inadequate professional growth. …”

Supplementary file 1_Educators’ reflections on AI-automated feedback in higher education: a structured integrative review of potentials, pitfalls, and ethical dimensions.docx by Latifah Hamdan Alghamdi (22611518)

“…Research indicates that AI can enhance customization, deliver immediate feedback, optimize repetitive processes, and increase student engagement. Nonetheless, these advantages are persistently compromised by concerns regarding algorithmic bias, data privacy, the deterioration of teacher-student relationships, and inadequate professional growth. …”

Supplementary file 1_Development of a venous thromboembolism risk prediction model for patients with primary membranous nephropathy based on machine learning.docx by Lian Li (49049)

“…The data was divided into training and test sets at an 8:2 ratio, followed by processed using combinations of three imputation methods, three sampling methods, and three feature selection methods. …”

Supplementary file 1_Integrating bioinformatics and molecular experiments to reveal the critical role of the cellular energy metabolism-related marker PLA2G1B in COPD epithelial ce... by Jun Shi (289433)

“…</p>Material and methods<p>This research identified cell energy metabolism-related differentially expressed genes (CEM-DEGs) by collecting CEM-associated signatures from multiple public databases and integrating these markers with data from the GEO database. Subsequently, five machine learning algorithms—Boruta, Xgboost, GBM, SVM-RFE, and LASSO—were employed to screen for key variables. …”

Table 1_Integrating bioinformatics and molecular experiments to reveal the critical role of the cellular energy metabolism-related marker PLA2G1B in COPD epithelial cells.xlsx by Jun Shi (289433)

“…</p>Material and methods<p>This research identified cell energy metabolism-related differentially expressed genes (CEM-DEGs) by collecting CEM-associated signatures from multiple public databases and integrating these markers with data from the GEO database. Subsequently, five machine learning algorithms—Boruta, Xgboost, GBM, SVM-RFE, and LASSO—were employed to screen for key variables. …”

AI in Education by Shynggys Toktas (22633589)

“…In addition, it highlights potential risks and ethical challenges, including data privacy concerns, algorithmic bias, and the impact of generative AI tools on academic integrity.…”

Methodological flowchart outlining the study’s workflow. by Ezekiel Ahn (6697256)

“…<p>The process begins with data acquisition from field trials. …”

Image 1_Immune-molecular nexus in reproductive disorders: mechanisms linking POI and RSA.pdf by Chen Chen (6544)

“…</p>Objective<p>This study aims to identify new central genes and potential therapeutic drugs for RSA and POI by integrating multi transcriptome data and machine learning algorithms.</p>Methods<p>This study utilized RNA sequencing data from patients with POI and RSA to identify key hub genes associated with these diseases. …”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…Details on the data sourcing process, prompt engineering strategies for large language model (LLM)-based extraction, and validation protocols are provided in the Supplementary Information section.…”