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Dataset results
189 results for “Treatment response prediction”
Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors
<p>Data required to reproduce the results/figures of the "<strong>Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors</strong>" project. </p>
Data from: Serum metabolomics predicts treatment response in myasthenia gravis
<p>High-dose prednisone is the primary initial therapy for myasthenia gravis (MG) but upwards of a third of patients do not respond to treatment. No biomarkers can predict clinical responsiveness to corticosteroid treatment. We conducted a discovery-based study to identify treatment responsive biomarkers in MG using sera obtained at study entry to the thymectomy clinical trial (MGTX), an NIH-sponsored randomized, controlled study of thymectomy plus prednisone versus prednisone alone. We applied ultra-performance liquid chromatography coupled with electro-spray quadrupole time of flight mass spectrometry to obtain comparative serum metabolomic and lipidomic profiles at study entry to correlate with treatment response at 6 months. Treatment response was assessed using validated outcome measures of minimal manifestation status (MMS), MG-Activities of Daily Living (MG-ADL), Quantitative MG (QMG) score, or a strictly defined composite measure of response. Increased levels of phospholipids were associated with treatment response as assessed by QMG, MMS, and the Responders classification, but all measures showed limited overlap in metabolomic profiles, in particular the MG-ADL. A panel including histidine, free fatty acid (13:0), <span>γ-</span>cholestenol and guanosine was highly predictive of the strictly defined treatment response measure. The AUC in Responders' prediction for these markers was 0.90. Pathway analysis suggests that xenobiotic metabolism could play a major role in treatment resistance. We have defined a metabolomic and lipidomic profile that can now undergo validation as treatment predictive markers for MG patients undergoing corticosteroid therapy. Distinct metabolomic profiles were appreciated for each outcome measure.</p>
[18F]FLT-PET as a Predictive Imaging Biomaker of Treatment Responses to Regorafenib
ClinicalTrials.gov study NCT02175095. IPD Sharing: Not stated. Countries: 1. Publications: 27.
Prediction of Clinical Response to SSRI Treatment in Bipolar Disorder Using Serotonin 1A Receptor PET Imaging
ClinicalTrials.gov study NCT02473250. IPD Sharing: Not stated. Countries: 1. Publications: 2.
FDG PET and DCE-MRI in Predicting Response to Treatment in Patients With Breast Cancer
ClinicalTrials.gov study NCT01931709. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Identifying Factors That Predict Antidepressant Treatment Response
ClinicalTrials.gov study NCT00360399. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Positron Emission Tomography in Predicting Response in Patients Who Are Undergoing Treatment With Pemetrexed Disodium and Cisplatin With or Without Surgery for Stage I, Stage II, or Stage III Non-Smal
ClinicalTrials.gov study NCT00227539. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Serum metabolomics predicts treatment response in myasthenia gravis
Open the record for dataset details and reuse information.
Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses
<p>Rscript for figure generation and data analysis:</p> <p>GenerateFigures.R</p> <p> </p> <p>Seurat objects containing processed data after quality control:</p> <p><a href="../api/records/10867209/draft/files/NCCS_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">NCCS_For_Zenodo.RDS</a> - NCCS discovery cohort.</p> <p><a href="../api/records/10867209/draft/files/Pavia_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">Pavia_For_Zenodo.RDS</a> - Pavia validation cohort.</p> <p> </p> <p>RDS files containing DEGs or differentially abundant surface markers:</p> <p>TestResults2Groups.rds - Cell type specific LTR vs Non Responder DEG </p> <p>TestResults2GroupsADT.rds - Cell type specific LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnly.rds - Cell type specific LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyADT.rds - Cell type specific LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3Groups.rds - Cell type specific LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p>TestResults2GroupsGeneralRNA.rds - Across cell type LTR vs Non Responder DEG </p> <p>TestResults2GroupsGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnlyGeneralRNA.rds - Across cell type LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3GroupsGeneralRNA.rds - Across cell type LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p> </p> <p>Logistic regression models trained on the NCCS discovery cohort:</p> <p>PerCellPredictions <CellType> * - Celltype specific models predicting either LTR, R or control group trained on all NCCS samples</p> <p>PerCellPredictions_2Groups_LungOnly <CellType> * - Celltype specific models predicting either LTR or NonResponder group, trained on lung samples only.</p> <p>PerCellPredictions_2Groups_<CellType> * - Celltype specific models predicting either LTR or NonResponder trained on all NCCS samples</p>
Clinical Dataset for the Paper 'Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis'
<p>This dataset accompanies the manuscript titled "Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis." It includes clinical data used for training and evaluating the machine learning models described in the paper. The dataset contains baseline clinical data of 154 RA patients who were treated with bDMARDs. The labels for remission, and effectiveness (remission and low disease activity) were applied after a 6-month follow-up based on EULAR criteria on DAS28ESR. The sustained effectiveness label indicates maintaining effectiveness within 6 months after initially achieving effectiveness.</p> <p><strong>Crossponder Authors:</strong></p> <ul> <li>Fatemeh Salehi (email: <a rel="noreferrer">fatemeh.salehihafshejni@fau.de</a>)</li> </ul>
The Obesity-hypoventilation Syndrome Study of Clinical Characteristics and Predictive Factors of Response to Treatment
ClinicalTrials.gov study NCT00938977. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Can Genetics Predict Treatment Response to a Computerized Self-help Program for Depression?
ClinicalTrials.gov study NCT01818453. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prediction of Response to Treatment of Patients With Chronic HCV Infection by Genetic Profile
ClinicalTrials.gov study NCT00272389. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Integrating Multimodal AI to Predict Treatment Response and Refine Risk Stratification in Esophageal Cancer
ClinicalTrials.gov study NCT07354295. IPD Sharing: NO. Countries: 1. Publications: 1.
Use Peripheral Blood Proteomics to Predict the Treatment Response and Toxicities in NSCLC Immunotherapy
ClinicalTrials.gov study NCT03951012. IPD Sharing: NO. Countries: 1. Publications: 2.
Predicting Treatment Response in Patients With OCD
ClinicalTrials.gov study NCT03993535. IPD Sharing: NO. Countries: 4. Publications: 25.
Study of Blood Components as Probable Prognostic and Predictive Markers of Response to Treatment in Advanced Colon and Rectal Cancers
ClinicalTrials.gov study NCT02979470. IPD Sharing: NO. Countries: 1. Publications: 2.
Response Prediction for Anti-angiogenic Treatment in Recurrent Glioblastoma
ClinicalTrials.gov study NCT04143425. IPD Sharing: NO. Countries: 1. Publications: 6.
Predicting Alcoholics' Treatment Responses to a Selective Serotonin Re-uptake Inhibitor (SSRI)
ClinicalTrials.gov study NCT00249405. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Validate Gene Expression and Proteomic Signatures Predictive of Treatment for Response for Breast Cancer Patient
ClinicalTrials.gov study NCT00669773. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.