Dataset for ALS Progression Prediction Study

Dataset for ALS Progression Prediction Study

<p dir="ltr">This dataset contains numerical clinical measurements related to ALS patients, which is suitable for machine learning analysis. The data is organized for progression-prediction of ALS and cohort classification.</p>

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Bibliografiske detaljer
Hovedforfatter:	SWASTHIKA BHASKAR (22677956) (author)
Udgivet:	2025
Fag:	Artificial intelligence not elsewhere classified Neural networks Machine learning not elsewhere classified ALS Clinical datasets numerical features Machine learning Progression prediction Cohort classification
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Lignende værker

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Figure 1 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
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Figure 2 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
af: David Milewski (15050643)
Udgivet: (2025)

Figure 3 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
af: David Milewski (15050643)
Udgivet: (2025)

Figure 4 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
af: David Milewski (15050643)
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Figure 5 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
af: David Milewski (15050643)
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Figure 6 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
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Supplementary Table S1-S3 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
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Table S2 from Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma
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Supplementary File 3 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
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Supplementary File 4 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
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Image 1_A machine learning-based predictive model for 48-week hepatitis B surface antigen seroclearance in chronic hepatitis B patients treated with pegylated interferon α-2b: prediction at week 24.tif
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Supplementary Table S6 from Blood-Based Diagnosis and Risk Stratification of Patients with Pancreatic Intraductal Papillary Mucinous Neoplasm (IPMN)
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Additional file 1 of Machine learning model for predicting severe infection in children with idiopathic nephrotic syndrome: multicenter retrospective study
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