Data Sheet 1_Exploring shared biomarkers and shared pathways in insomnia and atherosclerosis using integrated bioinformatics analysis.docx by Qichong Yang (19816146)

“…Our study aimed to explore the shared pathways and diagnostic biomarkers of ISM-related AS using integrated bioinformatics analysis.</p>Methods<p>We download the datasets from the Gene Expression Omnibus database and the GeneCards database. …”

Image 1_Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study.tif by Shuyu Wen (15411107)

“…This study aims to employ machine learning algorithms to establish a practical platform for the prediction of reintubation.…”

Image 2_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.tif by Zhi-Chuan He (21563657)

“…This study aimed to identify T cell-related signatures for TNBC diagnosis and prognosis.</p>Methods<p>Clinical data and transcriptomic profiles were obtained from the TCGA-BRCA dataset, and single-cell RNA sequencing (scRNA-seq) data were downloaded from the GEO database. …”

Data Sheet 1_A multi-cohort validated OXPHOS signature predicts survival and immune profiles in grade II/III glioma patients.csv by Jun Mou (4113313)

“…The immune cell composition and tumor microenvironment (TME) characteristics were assessed using ESTIMATE, MCPcounter, and CIBERSORT algorithms. Based on prognostic DEGs, we constructed a four-gene prognostic signature (MAOB, IGFBP2, SERPINA1, and LGR6).…”

Data Sheet 1_Multi-omics analysis reveals ultraviolet response insights for immunotherapy and prognosis.csv by DanHua Zhang (22315099)

“…Key genes (Hub-UVR.Sig) were identified via six machine learning algorithms, and breast cancer (BRCA) subtypes were classified through consensus clustering. …”

Image 6_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

“…Intercellular communication was analyzed using CellChat, while machine learning, incorporating seven different algorithms, was applied to identify key regulatory genes.…”

Image 1_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.pdf by Yi Li (1144)

“…Thus, the aim of this study was to highlight prevention and early detection opportunities in high-risk populations by identifying common biomarkers for T1DM and ccRCC.</p>Methods<p>Based on multiple publicly available datasets, WGCNA was applied to identify gene modules closely associated with T1DM, which were then integrated with prognostic DEGs in ccRCC. …”

Table 1_Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest.... by Qiang Wu (31071)

“…Objective<p>In this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP.…”

An open-pit mine segmentation dataset for deep learning by Lin Gang (18600526)

“…It was developed through a systematic process. Firstly, by conducting comprehensive literature research, the Point of Interest (POI) data of open-pit mines was summarized. …”

Table 1_Using machine learning to predict the rupture risk of multiple intracranial aneurysms.xlsx by Junqiang Feng (10300150)

“…By analyzing detailed morphological and anatomical parameters, our model provides a tailored approach to rupture risk assessment in MIAs, offering potential improvements over existing methods.</p>Methods<p>To address dataset imbalance, we conducted five-fold cross-validation. …”

Image 3_Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression.tif by Kun Guo (283211)

“…Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.</p>Objective<p>The aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.…”

Image 2_Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression.tif by Kun Guo (283211)

“…Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.</p>Objective<p>The aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.…”

Data Sheet 1_Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression.zip by Kun Guo (283211)

“…Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.</p>Objective<p>The aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.…”

Image 1_Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression.tif by Kun Guo (283211)

“…Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.</p>Objective<p>The aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.…”

Image 1_Safety assessment of temozolomidee: real-world adverse event analysis from the FAERS database.png by Yu Liu (6938)

“…Although temozolomidee has a certain efficacy in the treatment of brain malignancies, its numerous adverse effects (AEs) suggest that its safety needs to be thoroughly evaluated.</p>Methods<p>Based on data from the FDA Adverse Event Reporting System (FAERS) database, a retrospective pharmacovigilance study was conducted to evaluate temozolomide-related adverse events. …”

Agentic Architecture: Synthesising Complexity for Regenerative Futures by Alisa Andrasek (18028282)

“…It introduces a novel workflow linking n-dimensional voxelised data, multimodal generative AI, and generative algorithmic systems, including simulations of electromagnetic fields, site-adaptive Multi-Agent Systems, and field-based logics. …”

Data Sheet 2_Integrative analysis of single-cell and microarray data reveals SPI1-centered macrophage regulatory signatures in ulcerative colitis.zip by Yeqing Yu (7147064)

“…Using WGCNA on microarray data, we identified key downstream regulatory target genes, specifically IRAK3, IL1RN, CD55 and PEA15, based on microarray data. Their potential as biomarkers was subsequently validated through several machine learning algorithms. …”

Data Sheet 4_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.pdf by Binyu Wang (7375019)

“…DEGs were identified using GEO2R, filtered based on criteria of P < 0.05 and log2 fold change ≥ 1. …”

Table 1_Integrative analysis of single-cell and microarray data reveals SPI1-centered macrophage regulatory signatures in ulcerative colitis.csv by Yeqing Yu (7147064)

“…Using WGCNA on microarray data, we identified key downstream regulatory target genes, specifically IRAK3, IL1RN, CD55 and PEA15, based on microarray data. Their potential as biomarkers was subsequently validated through several machine learning algorithms. …”

Data Sheet 5_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.zip by Binyu Wang (7375019)

“…DEGs were identified using GEO2R, filtered based on criteria of P < 0.05 and log2 fold change ≥ 1. …”