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source dataset  (Kaggle Inc)

 
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    Kaggle Inc source dataset
    Source Dataset, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/source+dataset/access+dataset+open/pmc12996661-35-10-13
    Average 86 stars, based on 1 article reviews
    source dataset - by Bioz Stars, 2026-10
    86/100 stars

    Images

    Related Articles

    other:

    Article Title: X-ViTCNN: A Novel Network-Level Fusion of Transfer Learning and Customized Vision Transformer for Multi-Stage Alzheimer’s Disease Prediction Using MRI Scans
    Article Snippet: The author utilized an open-source dataset taken from Kaggle, including MR brain images, with an accuracy of 94%.

    Article Title: An Ensemble Model-Based Recommendation Approach for Consumer Decision-Making System
    Article Snippet: The proposed model is trained and tested using an open-source dataset on Kaggle's website.

    Article Title: Generative adversarial networks and hyperparameter-optimized XGBoost for enhanced heart disease prediction
    Article Snippet: In this research study, an open-source dataset has been used, which is freely available on Kaggle at the following link: https://www.kaggle.com/code/alawdisoft/personal-key-indicators-of-heart-diseases/notebook? select=heart\_2020\_cleaned.csvKaggle Notebook: Personal Key Indicators of Heart Diseases.

    Article Title: Developing Synthetic Orthopantomogram Datasets Through Generative Models
    Article Snippet: Additionally, OPGs from the TUFTS Dental Dataset (TDD) and an open-source dataset from Kaggle were used to increase the diversity of the images., , A total of 5,383 OPGs of permanent dentition were included in the model training.

    Article Title: Generative adversarial networks and hyperparameter-optimized XGBoost for enhanced heart disease prediction.
    Article Snippet: In this research study, an open-source dataset has been used, which is freely available on Kaggle at the following link: https://www.kaggle.com/code/alawdisoft/personal-key-indicators-of-heartdiseases/notebook?select=heart_2020_cleaned.csvKaggle Notebook: Personal Key Indicators of Heart Diseases

    Magnetic Resonance Imaging:

    Article Title: X-ViTCNN: A Novel Network-Level Fusion of Transfer Learning and Customized Vision Transformer for Multi-Stage Alzheimer's Disease Prediction Using MRI Scans.
    Article Snippet: .. The author utilized an open-source dataset taken from Kaggle, including MR brain images, with an accuracy of 94%. https://doi.org/10.3390/diagnostics16060835 In addition, Rallabandi et al. [26] developed an Inception-ResNet wrapper model to classify AD using MRI and PET scans. ..

    Positron Emission Tomography:

    Article Title: X-ViTCNN: A Novel Network-Level Fusion of Transfer Learning and Customized Vision Transformer for Multi-Stage Alzheimer's Disease Prediction Using MRI Scans.
    Article Snippet: .. The author utilized an open-source dataset taken from Kaggle, including MR brain images, with an accuracy of 94%. https://doi.org/10.3390/diagnostics16060835 In addition, Rallabandi et al. [26] developed an Inception-ResNet wrapper model to classify AD using MRI and PET scans. ..



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    open source dataset gse4648  (Biotechnology Information)
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    a ) and b ) Gene expression changes within infarcted left ventricle (‘infarct’) are marked at 24- and 48-hours post-MI, as assessed by hierarchical clustering and principal component analyses of the open-source GEO dataset <t>GSE4648</t> . Analyses carried out using Geo2R and the open-source BART bioinformatics tool. c ) Differential gene expression patterns identified in 24-hour post-MI ventricle compared with sham control. cIAP2 (Birc3) is significantly upregulated. d ) LV infarct region gene expression timecourse of cIAP2 (maroon) and related transcripts cIAP1 (blue) and Xiap (green), contrasted with measured cardiac marker tnnt2 (Cardiac Troponin T) and inflammatory marker cxcl1. e ) Compiled expression timecourse of representative myeloid-related transcripts (maroon) and lymphoid transcripts (blue) from LV infarct region post-MI. ptprc (black) = hematopoietic marker gene transcript. f ) Gene Ontogeny (GO) pathway enrichment following MI identifies multiple networks requiring cIAP2. g ) NOD1 −/− mice have reduced systolic dysfunction and cardiac inflammation following MI relative to WT control mice. Control-operated mice: N = 3; MI-operated mice: N = 5. Ejection fraction: N = 4 MI-operated per group. h ) cIAP2 is expressed at lower levels, along with inflammatory genes RIPK1, IRAK4 and TRAF6, in multiple danger signaling receptor-deficient mouse models following MI. Image is representative of two similar experiments. Error bars denote Mean +/- SD; p values were calculated using one-way ANOVA with Bonferroni’s correction ( g , upper panel) or two-sided t-test ( g , lower panel).
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    a ) and b ) Gene expression changes within infarcted left ventricle (‘infarct’) are marked at 24- and 48-hours post-MI, as assessed by hierarchical clustering and principal component analyses of the open-source GEO dataset <t>GSE4648</t> . Analyses carried out using Geo2R and the open-source BART bioinformatics tool. c ) Differential gene expression patterns identified in 24-hour post-MI ventricle compared with sham control. cIAP2 (Birc3) is significantly upregulated. d ) LV infarct region gene expression timecourse of cIAP2 (maroon) and related transcripts cIAP1 (blue) and Xiap (green), contrasted with measured cardiac marker tnnt2 (Cardiac Troponin T) and inflammatory marker cxcl1. e ) Compiled expression timecourse of representative myeloid-related transcripts (maroon) and lymphoid transcripts (blue) from LV infarct region post-MI. ptprc (black) = hematopoietic marker gene transcript. f ) Gene Ontogeny (GO) pathway enrichment following MI identifies multiple networks requiring cIAP2. g ) NOD1 −/− mice have reduced systolic dysfunction and cardiac inflammation following MI relative to WT control mice. Control-operated mice: N = 3; MI-operated mice: N = 5. Ejection fraction: N = 4 MI-operated per group. h ) cIAP2 is expressed at lower levels, along with inflammatory genes RIPK1, IRAK4 and TRAF6, in multiple danger signaling receptor-deficient mouse models following MI. Image is representative of two similar experiments. Error bars denote Mean +/- SD; p values were calculated using one-way ANOVA with Bonferroni’s correction ( g , upper panel) or two-sided t-test ( g , lower panel).
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    Image Search Results


    a ) and b ) Gene expression changes within infarcted left ventricle (‘infarct’) are marked at 24- and 48-hours post-MI, as assessed by hierarchical clustering and principal component analyses of the open-source GEO dataset GSE4648 . Analyses carried out using Geo2R and the open-source BART bioinformatics tool. c ) Differential gene expression patterns identified in 24-hour post-MI ventricle compared with sham control. cIAP2 (Birc3) is significantly upregulated. d ) LV infarct region gene expression timecourse of cIAP2 (maroon) and related transcripts cIAP1 (blue) and Xiap (green), contrasted with measured cardiac marker tnnt2 (Cardiac Troponin T) and inflammatory marker cxcl1. e ) Compiled expression timecourse of representative myeloid-related transcripts (maroon) and lymphoid transcripts (blue) from LV infarct region post-MI. ptprc (black) = hematopoietic marker gene transcript. f ) Gene Ontogeny (GO) pathway enrichment following MI identifies multiple networks requiring cIAP2. g ) NOD1 −/− mice have reduced systolic dysfunction and cardiac inflammation following MI relative to WT control mice. Control-operated mice: N = 3; MI-operated mice: N = 5. Ejection fraction: N = 4 MI-operated per group. h ) cIAP2 is expressed at lower levels, along with inflammatory genes RIPK1, IRAK4 and TRAF6, in multiple danger signaling receptor-deficient mouse models following MI. Image is representative of two similar experiments. Error bars denote Mean +/- SD; p values were calculated using one-way ANOVA with Bonferroni’s correction ( g , upper panel) or two-sided t-test ( g , lower panel).

    Journal: Nature Cardiovascular Research

    Article Title: Hematopoietic expression of cIAP2 drives inflammation and heart failure after myocardial infarction

    doi: 10.1038/s44161-026-00782-x

    Figure Lengend Snippet: a ) and b ) Gene expression changes within infarcted left ventricle (‘infarct’) are marked at 24- and 48-hours post-MI, as assessed by hierarchical clustering and principal component analyses of the open-source GEO dataset GSE4648 . Analyses carried out using Geo2R and the open-source BART bioinformatics tool. c ) Differential gene expression patterns identified in 24-hour post-MI ventricle compared with sham control. cIAP2 (Birc3) is significantly upregulated. d ) LV infarct region gene expression timecourse of cIAP2 (maroon) and related transcripts cIAP1 (blue) and Xiap (green), contrasted with measured cardiac marker tnnt2 (Cardiac Troponin T) and inflammatory marker cxcl1. e ) Compiled expression timecourse of representative myeloid-related transcripts (maroon) and lymphoid transcripts (blue) from LV infarct region post-MI. ptprc (black) = hematopoietic marker gene transcript. f ) Gene Ontogeny (GO) pathway enrichment following MI identifies multiple networks requiring cIAP2. g ) NOD1 −/− mice have reduced systolic dysfunction and cardiac inflammation following MI relative to WT control mice. Control-operated mice: N = 3; MI-operated mice: N = 5. Ejection fraction: N = 4 MI-operated per group. h ) cIAP2 is expressed at lower levels, along with inflammatory genes RIPK1, IRAK4 and TRAF6, in multiple danger signaling receptor-deficient mouse models following MI. Image is representative of two similar experiments. Error bars denote Mean +/- SD; p values were calculated using one-way ANOVA with Bonferroni’s correction ( g , upper panel) or two-sided t-test ( g , lower panel).

    Article Snippet: Left ventricular expression of gene products over a 48-hour timecourse was catalogued in the National Center for Biotechnology Information (NCBI)-curated, open-source dataset GSE4648 ( GDS2329 ) (for reference, see https://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS2329 ).

    Techniques: Gene Expression, Control, Marker, Expressing

    The monkeypox lesion and other skin image analysis.

    Journal: Digital Health

    Article Title: Detection of monkeypox skin lesions using edge enhancement algorithms integrated with hybrid deep learning

    doi: 10.1177/20552076261415651

    Figure Lengend Snippet: The monkeypox lesion and other skin image analysis.

    Article Snippet: In this study, an open-source Kaggle Monkeypox Image dataset was utilized.

    Techniques: