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58 results for “Patient classification”
Dataset for Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients
<p>This dataset contains all the spectra used in the paper "Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients", plus the R code to import the TXT (ASCII) files into a dataset, preprocess data, set-up and cross validate the PCA-LDA model and generate the figures shown in the paper.</p> <p>Data are available in 2 different formats: </p> <p>- 1 compressed archive ("dataset.zip") containing all the 144 TXT files (1 file = 1 spectrum) </p> <p>- 1 single CSV file (“dataset.csv”) with all the 144 spectra in the form of a table. The data are structured as follow, with each row being 1 spectrum, preceded by metadata: "acquisition_date", "substrate_batch", "class", "sample_code".</p> <p>The code for R is available as a single file "Rcode.R".</p> <p> </p>
netDx: Interpretable patient classification using integrated patient similarity networks
<p>Docker image containing installed netDx software in Ubuntu to reproduce examples from the published manuscript. The R implementation of netDx is hosted at: https://github.com/BaderLab/netDx</p> <p>---<br> Publication abstract: Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be easily interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks. netDx meets the above criteria and particularly excels at data integration and model interpretability. We compared classification performance of this method against other machine-learning algorithms, using a cancer survival benchmark with four cancer types, each requiring integration of up to six genomic and clinical data types. In these tests, netDx has significantly higher average performance than most other machine-learning approaches across most cancer types. In comparison to traditional machine learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in diverse data sets of breast cancer and asthma. Thus, netDx can serve both as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a freely available software implementation of netDx along with sample files and automation workflows in R.</p>
IMBALANCED MACHINE LEARNING CLASSIFICATION MODELS FOR REMOVAL BIOSIMILAR DRUGS AND INCREASED ACTIVITY IN PATIENTS WITH RHEUMATIC DISEASES
<p>Objective: Predict long-term disease worsening and the removal of biosimilar medication in patients with rheumatic diseases.</p><p>Methodology: Observational, retrospective, and descriptive study. Review of a database of patients with immune-mediated inflammatory rheumatic diseases. Disease worsening and removing biosimilars are imbalanced variables, that require using imbalanced machine learning models selected based on their superior f1-scores and great accuracy. Previously, we selected the most important variables using mutual information tests.</p><p>Results: The best imbalanced machine learning models to predict disease worsening and the removal of the biosimilar obtained f1-scores of 0.52 and 0.63, respectively. Both models are decision trees. In the first one, two important factors are switching of biosimilar and age, and in the second, the relevant variables are optimization and the value of the initial CRP. </p><p>Conclusions: Biosimilar drugs do not always work well for rheumatic diseases. We obtained two imbalanced machine learning models to detect those cases, where the drug should be removed or where the activity of the disease increases from low to high. Our decision trees use variables, such as age or switching, not considered in previous studies.</p>
Deep Neural Network for Stroke Patient Gait Analysis and Classification
ClinicalTrials.gov study NCT04968418. IPD Sharing: NO. Countries: 1. Publications: 3.
Microenvironment characteristics and molecular classification in pheochromocytoma patients
<p>Pheochromocytomas (PCCs) are rare neuroendocrine tumors that originate from chromaffin cells in the adrenal gland. However, the cellular molecular characteristics and immune microenvironment of PCCs are incompletely understood. Here, we performed single-cell RNA sequencing (scRNA-seq) on 16 tissues from 4 sporadic unclassified PCC patients and 1 hereditary PCC patient with Von Hippel-Lindau (VHL) syndrome. We found that intra-tumoral heterogeneity was less extensive than the inter-individual heterogeneity of PCCs. Further, the unclassified PCC patients were divided into two types, metabolism-type (marked by NDUFA4L2 and COX4I2) and kinase-type (marked by RET and PNMT), validated by immunohistochemical staining. Trajectory analysis of tumor evolution revealed that metabolism-type PCC cells display phenotype of consistently active metabolism and increased metastasis potential, while kinase-type PCC cells showed decreased epinephrine synthesis and neuron-like phenotypes. Cell-cell communication analysis showed activation of the annexin pathway and a strong inflammation reaction in metabolism-type PCCs and activation of FGF signaling in the kinase-type PCC. Although multispectral immunofluorescence staining showed a lack of CD8<sup>+</sup> T cell infiltration in both metabolism-type and kinase-type PCCs, only the kinase-type PCC exhibited downregulation of <em>HLA-Ⅰ</em> molecules that possibly regulated by <em>RET</em>, suggesting the potential of combined therapy with kinase inhibitors and immunotherapy for kinase-type PCCs; in contrast, the application of immunotherapy to metabolism-type PCCs (with antigen presentation ability) is likely unsuitable. Our study presents a single-cell transcriptomics-based molecular classification and microenvironment characterization of PCCs, providing clues for potential therapeutic strategies to treat PCCs.</p>
Lauren Classifications and HER2 Status in Gastric Cancer Patients
ClinicalTrials.gov study NCT01927146. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Oxford Classification and Clinical Remission After Initial Treatments in Patients With IgA Nephropathy
ClinicalTrials.gov study NCT05528991. IPD Sharing: YES. Countries: 1. Publications: 4.
Sensor-supported Classification of Gait Patterns in Everyday Movement of Patients With Parkinson's Disease
ClinicalTrials.gov study NCT04054856. IPD Sharing: NO. Countries: 1. Publications: 8.
Integrating Artificial Intelligence Into International Classification of Functioning, Disability, and Health Coding: Effectiveness of a Mobile Application for Patient Questionnaires
ClinicalTrials.gov study NCT07021781. IPD Sharing: NO. Countries: 1. Publications: 1.
SAGIT for Classification of Patients With Acromegaly in Clinical Practice
ClinicalTrials.gov study NCT02231593. IPD Sharing: Not stated. Countries: 7. Publications: 1.
Airway Ultrasonography Measurements With Cormack Lehane Classification in Patients With OSA
ClinicalTrials.gov study NCT03899519. IPD Sharing: NO. Countries: 1. Publications: 3.
Efficacy of Classification Based 'Cognitive Functional Therapy' in Patients With Non Specific Chronic Low Back Pain
ClinicalTrials.gov study NCT01129817. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Core Muscles Training On Patients With Chronic Mechanical Low Back Pain According To SALIBA'S Postural Classification System
ClinicalTrials.gov study NCT06296667. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Risk-Group Classification of Patients With Newly Diagnosed Acute Lymphoblastic Leukemia
ClinicalTrials.gov study NCT00482352. IPD Sharing: Not stated. Countries: 6. Publications: 1.
Hypotonic Hyponatremia: Criteria for the Correct Classification of Its Etiology and of Patient Volume Status
ClinicalTrials.gov study NCT04402190. IPD Sharing: NO. Countries: 1. Publications: 8.
Microenvironment characteristics and molecular classification in pheochromocytoma patients
Open the record for dataset details and reuse information.
scPanel: A tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets
<p>Dataset used to reproduce severe COVID-19 prediction results in scPanel manuscript.</p>
Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery
ClinicalTrials.gov study NCT03724123. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Data from: Cross-sectional study of patients with axial spondyloarthritis fulfilling imaging arm of ASAS classification criteria: baseline clinical characteristics and subset differences in a single centre cohort
Open the record for dataset details and reuse information.
Expression profiles of 30-gene hepatocellular carcinoma molecular classification signature in surgically resected Japanese hepatocellular carcinoma patients
GEO Series GSE59548. Homo sapiens. 96 samples. Type: Expression profiling by array.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.