Table 1_Correlation between blood urea nitrogen/albumin levels and 30-day all-cause mortality in critically Ill patients with heart failure: a retrospective cohort study and predic... by Wen-Ting Sun (7378373)

“…Nine machine learning (ML) algorithms were used to build predictive models, and, in addition, the Shapley additive interpretation (SHAP) method was used to determine feature importance.…”

Supplementary file 2_Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.xlsx by LiHan Lin (19267964)

“…Predictive models for fall risk over the next 3 years among urban and rural older populations were developed using five machine learning algorithms. Logistic regression analysis was employed to identify key factors influencing falls in these populations.…”

Supplementary file 3_Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.xlsx by LiHan Lin (19267964)

“…Predictive models for fall risk over the next 3 years among urban and rural older populations were developed using five machine learning algorithms. Logistic regression analysis was employed to identify key factors influencing falls in these populations.…”

Supplementary file 1_Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.pdf by LiHan Lin (19267964)

“…Predictive models for fall risk over the next 3 years among urban and rural older populations were developed using five machine learning algorithms. Logistic regression analysis was employed to identify key factors influencing falls in these populations.…”

Table 1_Ethical and legal concerns in artificial intelligence applications for the diagnosis and treatment of lung cancer: a scoping review.docx by Ghenwa Chamouni (22425346)

“…</p>Results<p>The most frequently reported ethical concern was data privacy. Other recurrent issues included informed consent, no harm to patients, algorithmic bias and fairness, transparency, equity in AI access and use, and trust. …”

Josefina Barrera Morelli: Lectures on cheMOOmetrics: udderly accurate whey to predict by Josefina Barrera Morelli (14074149)

“…For this purpose, I have evaluated various prepocessing techniques that are applied over the MIR spectra. Subsequently used the preprocessed data is combined with information on detailed composition to develop predictive models with algorithms (mostly machine learning) in my computer, to try to obtain the best accuracies possible. …”

OHID-1: A New Large Hyperspectral Image Dataset for Multi-Classification by Jianwen Deng (19780538)

“…Furthermore, this study demonstrates the utility of OHID-1 by testing it with selected hyperspectral classification algorithms. This dataset will be useful to advance cutting-edge research in urban sustainable development science, land use analysis. …”

Image 5_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…</p>Methods<p>We integrated bulk transcriptome profiles from TCGA-GBM, CGGA, and GEO datasets with single-cell RNA sequencing data from GSE141383 and GSE223063. A comprehensive GBM single-cell atlas was constructed using Seurat and Harmony, and malignant epithelial cells were inferred via inferCNV. …”

Image 3_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…</p>Methods<p>We integrated bulk transcriptome profiles from TCGA-GBM, CGGA, and GEO datasets with single-cell RNA sequencing data from GSE141383 and GSE223063. A comprehensive GBM single-cell atlas was constructed using Seurat and Harmony, and malignant epithelial cells were inferred via inferCNV. …”

Table 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…</p>Methods<p>We integrated bulk transcriptome profiles from TCGA-GBM, CGGA, and GEO datasets with single-cell RNA sequencing data from GSE141383 and GSE223063. A comprehensive GBM single-cell atlas was constructed using Seurat and Harmony, and malignant epithelial cells were inferred via inferCNV. …”

Image 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…</p>Methods<p>We integrated bulk transcriptome profiles from TCGA-GBM, CGGA, and GEO datasets with single-cell RNA sequencing data from GSE141383 and GSE223063. A comprehensive GBM single-cell atlas was constructed using Seurat and Harmony, and malignant epithelial cells were inferred via inferCNV. …”

Image 4_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…</p>Methods<p>We integrated bulk transcriptome profiles from TCGA-GBM, CGGA, and GEO datasets with single-cell RNA sequencing data from GSE141383 and GSE223063. A comprehensive GBM single-cell atlas was constructed using Seurat and Harmony, and malignant epithelial cells were inferred via inferCNV. …”

Table 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…</p>Methods<p>We integrated bulk transcriptome profiles from TCGA-GBM, CGGA, and GEO datasets with single-cell RNA sequencing data from GSE141383 and GSE223063. A comprehensive GBM single-cell atlas was constructed using Seurat and Harmony, and malignant epithelial cells were inferred via inferCNV. …”

Image 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…</p>Methods<p>We integrated bulk transcriptome profiles from TCGA-GBM, CGGA, and GEO datasets with single-cell RNA sequencing data from GSE141383 and GSE223063. A comprehensive GBM single-cell atlas was constructed using Seurat and Harmony, and malignant epithelial cells were inferred via inferCNV. …”

COSMO-Bench by Daniel McGann (18759496)

“…<p dir="ltr"><b><i>Abstract</i></b>: Recent years have seen a focus on research into distributed optimization algorithms for multi-robot Collaborative Simultaneous Localization and Mapping (C-SLAM). …”

Overall model performance. by Theogene Kubahoniyesu (20391184)

“…This study aimed to predict these outcomes in Rwanda using supervised machine learning algorithms.</p><p>Methods</p><p>This cross-sectional study utilized data from the Rwanda Demographic and Health Survey (RDHS, 2019–2020) involving 14,634 women. …”

Overall model performance. by Theogene Kubahoniyesu (20391184)

“…This study aimed to predict these outcomes in Rwanda using supervised machine learning algorithms.</p><p>Methods</p><p>This cross-sectional study utilized data from the Rwanda Demographic and Health Survey (RDHS, 2019–2020) involving 14,634 women. …”

Supplementary Material for: Analytical validation of wrist-worn accelerometer-based step count methods during structured and free-living activities by Marcotte R.T. (20414558)

“…Four open-source methods implementing different algorithmic approaches were applied to CPIW data to derive step counts. …”

Datasheet1_Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.docx by Chor Yiu Chloe Chung (19986408)

“…Future research should focus on integrating diverse data types, including advanced imaging, semantics and clinical data while also aiming to standardise the collection and integration of multimodal data. …”

Table 12_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”