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>

Furkejuvvon:

Bibliográfalaš dieđut
Váldodahkki:	SWASTHIKA BHASKAR (22677956) (author)
Almmustuhtton:	2025
Fáttát:	Artificial intelligence not elsewhere classified Neural networks Machine learning not elsewhere classified ALS Clinical datasets numerical features Machine learning Progression prediction Cohort classification
Fáddágilkorat:	Lasit fáddágilkoriid Eai fáddágilkorat, Lasit vuosttaš fáddágilkora!

Geahča maid

<b>Axiom-Based AGI: A Quantum-Error-Corrected Architecture with Temporal Shard Memory</b>
Dahkki: Lakshit Mathur (20894549)
Almmustuhtton: (2025)

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
Dahkki: David Milewski (15050643)
Almmustuhtton: (2025)

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
Dahkki: David Milewski (15050643)
Almmustuhtton: (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
Dahkki: David Milewski (15050643)
Almmustuhtton: (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
Dahkki: David Milewski (15050643)
Almmustuhtton: (2025)

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
Dahkki: David Milewski (15050643)
Almmustuhtton: (2025)

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
Dahkki: David Milewski (15050643)
Almmustuhtton: (2025)

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
Dahkki: David Milewski (15050643)
Almmustuhtton: (2025)

Table S1 from Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma
Dahkki: Hanyun Zhang (15386757)
Almmustuhtton: (2025)

Table S2 from Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma
Dahkki: Hanyun Zhang (15386757)
Almmustuhtton: (2025)

Supplementary file 1_Bacteriocin prediction through cross-validation-based and hypergraph-based feature evaluation approaches.docx
Dahkki: Suraiya Akhter (22679093)
Almmustuhtton: (2025)

DATASET: Data-Driven Clinical Decision Trees and Generative Topographic Mapping to Guide Stroke Rehabilitation Program Allocation
Dahkki: Dimitrios-Sokratis Komaris (10266358)
Almmustuhtton: (2025)

Suppl Data 2 from Whole Slide Imaging-Based Prediction of <i>TP53</i> Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer
Dahkki: Marija Pizurica (17060224)
Almmustuhtton: (2025)

Suppl. Data 1 from Whole Slide Imaging-Based Prediction of <i>TP53</i> Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer
Dahkki: Marija Pizurica (17060224)
Almmustuhtton: (2025)

Direct Numerical Simulation Dataset of Turbulent Channel Flow at Re_tau=180
Dahkki: Pablo Portero (22639367)
Almmustuhtton: (2025)

Table 1_Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning.docx
Dahkki: Kai Xie (2053498)
Almmustuhtton: (2025)

Data Sheet 1_Universal medical image segmentation via in-context cross-attention.pdf
Dahkki: Costin F. Ciusdel (22679303)
Almmustuhtton: (2025)

Supplementary File 2 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
Dahkki: Charles Blatti (15292859)
Almmustuhtton: (2025)

Supplementary File 3 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
Dahkki: Charles Blatti (15292859)
Almmustuhtton: (2025)

Supplementary File 4 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
Dahkki: Charles Blatti (15292859)
Almmustuhtton: (2025)

Table 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.doc
Dahkki: Nan Kong (6917111)
Almmustuhtton: (2025)

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
Dahkki: Nan Kong (6917111)
Almmustuhtton: (2025)

Supplementary Table S4 from Blood-Based Diagnosis and Risk Stratification of Patients with Pancreatic Intraductal Papillary Mucinous Neoplasm (IPMN)
Dahkki: Chaoyang Zhang (15051876)
Almmustuhtton: (2025)

Supplementary Table S6 from Blood-Based Diagnosis and Risk Stratification of Patients with Pancreatic Intraductal Papillary Mucinous Neoplasm (IPMN)
Dahkki: Chaoyang Zhang (15051876)
Almmustuhtton: (2025)

Additional file 2 of Machine learning model for predicting severe infection in children with idiopathic nephrotic syndrome: multicenter retrospective study
Dahkki: Sijie Yu (8895605)
Almmustuhtton: (2025)

Additional file 1 of Machine learning model for predicting severe infection in children with idiopathic nephrotic syndrome: multicenter retrospective study
Dahkki: Sijie Yu (8895605)
Almmustuhtton: (2025)

Data Sheet 1_Enhancing preoperative HER2 status classification of invasive breast cancers using machine learning models based on clinicopathological and MRI features: a multicenter study.docx
Dahkki: Suhong Zhao (10057478)
Almmustuhtton: (2025)

اكتساب الإبداع الذاتي للذكاء الاصطناعي لصفة المؤلف في التشريع الجزائري
Dahkki: Tidjani, Bouzidi Ahmed
Almmustuhtton: (2025)

Supplementary Appendix S1 from DNA-Methylome–Based Tumor Hypoxia Classifier Identifies HPV-Negative Head and Neck Cancer Patients at Risk for Locoregional Recurrence after Primary Radiochemotherapy
Dahkki: Bouchra Tawk (15389486)
Almmustuhtton: (2025)

Machine Learning-Driven Cross-Species Toxicity Prediction for Advancing Ecologically Relevant PFAS Water Quality Criteria
Dahkki: Weigang Liang (734333)
Almmustuhtton: (2025)

<b>Minimal repeats identified by machine learning as novel cross-species markers of translation initiation sites</b>
Dahkki: Ali Mohammad Ali Maddi (11301486)
Almmustuhtton: (2025)

Table 1_Machine learning-based differentiation of lung squamous cell carcinoma and adenocarcinoma using clinical-semantic and radiomic features.docx
Dahkki: Yanju Li (9697993)
Almmustuhtton: (2025)

OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands
Dahkki: Rajkumar R (22674565)
Almmustuhtton: (2025)

OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands
Dahkki: Rajkumar R (22674565)
Almmustuhtton: (2025)

OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands
Dahkki: Rajkumar R (22674565)
Almmustuhtton: (2025)

OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands
Dahkki: Rajkumar R (22674565)
Almmustuhtton: (2025)

OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands
Dahkki: Rajkumar R (22674565)
Almmustuhtton: (2025)

OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands
Dahkki: Rajkumar R (22674565)
Almmustuhtton: (2025)

Data Sheet 1_Latent class analysis and machine learning for clinical subtyping prediction and differentiation in suspected neurosyphilis patients.pdf
Dahkki: Sirui Wu (22559208)
Almmustuhtton: (2025)

Video 1_A machine learning algorithm to predict treatment effectiveness for Kawasaki disease in China: a retrospective model development and validation study.mp4
Dahkki: Xuemei Li (232384)
Almmustuhtton: (2025)

Cannot write session to /tmp/vufind_sessions/sess_ria69pbkqtcc68m19uhcvjq8jf