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633
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633 results for “TCR”
CMV-TCR-T Cells for CMV Infection After Allogenic HSCT
ClinicalTrials.gov study NCT05140187. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Non-Viral TCR Gene Therapy
ClinicalTrials.gov study NCT04102436. IPD Sharing: YES. Countries: 1. Publications: 4.
Effect of Chidamide Combined With CAT-T or TCR-T Cell Therapy on HIV-1 Latent Reservoir
ClinicalTrials.gov study NCT03980691. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Creating an Innovative AI-based Technology TCRact for Designing and Optimizing T-cell Receptors (TCR) for Use in Cancer Immunotherapies
ClinicalTrials.gov study NCT04994093. IPD Sharing: Not stated. Countries: 1. Publications: 1.
MAGE-C2 TCR T Cell Trial to Treat Melanoma and Head and Neck Cancer
ClinicalTrials.gov study NCT04729543. IPD Sharing: NO. Countries: 1. Publications: 4.
TCR-T Cell Therapy on Advanced Solid Tumors
ClinicalTrials.gov study NCT05438667. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Memory T-cell Infusion to Improve Immunity After TCR-alpha/Beta Depleted Hematopoietic Stem Cell Transplantation
ClinicalTrials.gov study NCT02337595. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A Study of TCR-Redirected T Cell Infusion to Prevent Hepatocellular Carcinoma Recurrence Post Liver Transplantation
ClinicalTrials.gov study NCT02686372. IPD Sharing: NO. Countries: 1. Publications: 3.
A Study of Gene Edited Autologous Neoantigen Targeted TCR T Cells With or Without Anti-PD-1 in Patients With Solid Tumors
ClinicalTrials.gov study NCT03970382. IPD Sharing: NO. Countries: 1. Publications: 2.
Phase I Trial of LMP2 Antigen-specific TCR T-cell Therapy for Recurrent and Metastatic NPC Patients
ClinicalTrials.gov study NCT03925896. IPD Sharing: NO. Countries: 1. Publications: 13.
TCR-Redirected T Cells Therapy in Patient With HBV Related HCC
ClinicalTrials.gov study NCT03899415. IPD Sharing: NO. Countries: 1. Publications: 1.
Autologous HBV-specific T Cell Receptor Engineered T Cells (TCR-T) in Patients With HBV-related Advanced HCC
ClinicalTrials.gov study NCT05339321. IPD Sharing: Not stated. Countries: 1. Publications: 2.
TCR-engineered T Cells in Solid Tumors (ACTengine IMA201-101)
ClinicalTrials.gov study NCT03247309. IPD Sharing: NO. Countries: 2. Publications: 1.
TCR-Redirected T Cell Treatment in Patients With Recurrent HBV-related Hepatocellular Carcinoma Post Liver Transplantation
ClinicalTrials.gov study NCT04677088. IPD Sharing: Not stated. Countries: 1. Publications: 4.
E7 TCR-T Cell Immunotherapy for Human Papillomavirus (HPV) Associated Cancers
ClinicalTrials.gov study NCT05686226. IPD Sharing: YES. Countries: 1. Publications: 1.
HA-1 T TCR T Cell Immunotherapy for the Treatment of Patients With Relapsed or Refractory Acute Leukemia After Donor Stem Cell Transplant
ClinicalTrials.gov study NCT03326921. IPD Sharing: NO. Countries: 1. Publications: 1.
Trial to Assess the Efficacy of a TCR Alfa Beta Depleted Graft in Pediatric Affected by ALL or AML and Receiving an HSCT
ClinicalTrials.gov study NCT01810120. IPD Sharing: NO. Countries: 1. Publications: 29.
Data for "Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification" (ImRex)
<p>Repository containing the different experiments described in the manuscript titled: "Current challenges for epitope-agnostic TCR interaction prediction and a new perspective derived from image classification".</p> <p>Publication DOI: TBA</p> <p>Originally appeared as a preprint on bioRxiv: <a href="https://doi.org/10.1101/2019.12.18.880146">https://doi.org/10.1101/2019.12.18.880146</a>.</p> <p>Contains:</p> <ul> <li>Trained model files (.h5)</li> <li>Associated train and validation datasets for each model.</li> <li>Learning curves and evaluation metrics.</li> <li>Log files with training and data arguments (full training scripts are available in GitHub repository).</li> <li>Comparisons between different models.</li> <li>Complete raw and processed datasets (also available in the associated GitHub repository).</li> </ul> <p><strong>Please refer to the associated GitHub repository (<a href="https://github.com/pmoris/ImRex">https://github.com/pmoris/ImRex</a>) for more information on the directory structure and contents, as well as the scripts that generated these output files.</strong></p> <p><strong>Contents:</strong></p> <ul> <li> <p><code>data.zip</code>: Contains raw and preprocessed datasets. READMEs in subdirectory describe the data sources and preprocessing steps. Please refer to the associated GitHub repository for the specific scripts that generated these files. Note that the full training and test sets (i.e. containing both positive and negative examples) are stored separately for each model/CV iteration in the <code>models</code> archives.</p> </li> <li> <p><code>models-main.zip</code>: contains the trained models and evaluation metrics for the main different experiments described in the bash and pbs scripts in <code>./src/scripts/hpc_scripts</code>. Log files for the experiments outlined here can be found in <code>./src/scripts/hpc_scripts</code>.</p> </li> <li> <p><code>models-full.zip</code>: contains models that were trained on the complete VDJdb dataset without cross-validation, filtered on human TRB data, no 10x data and restricted to 10-20 (CDR3) or 8-11 (epitope) amino acid residues, with negatives that were generated by shuffling (i.e. sampling an negative epitope for each positive CDR3 sequence). One set of models uses downsampling to reduce the most abundant epitopes down to 400 pairs each, the other one does not use any downsampling. These models were also used for evaluating on the external Adaptive dataset, as outlined in <code>./src/scripts/evaluate/evaluate_adaptive.sh</code>, and the TRA subset of sequences (<code>./src/scripts/evaluate/evaluate_tra.sh</code>).</p> </li> <li> <p><code>models-decoyfit.zip</code>: contains models that were trained on true data, but evaluated on data where epitopes were replaced by decoys.</p> </li> <li> <p><code>models-padded-epitoperatio.zip</code>: contains a quick test of trained models (padded/interaction map) that use a different type of negative shuffling, see docstrings in <code>./src/processing/negative_sampler.py</code> for more info.</p> </li> <li> <p><code>models-repeat-local.zip</code>: contains a number of repeated runs from <code>models-main</code>, used to estimate variability in model performance for multiple identical runs.</p> </li> <li> <p><code>comparisons.zip</code>: contains comparison directories, each consisting of two or more model output directories, that contrast the performance metrics of the models. These outputs were generated by using the <code>./src/scripts/evaluate/visualize.py</code> script, or by using the oneliners in <code>./src/scripts/evaluate/visualise.sh</code>, which can operate on the entire comparisons directory at once.</p> </li> </ul> <p><strong>Note that any file paths described here are in reference to the associated GitHub repository (<a href="https://github.com/pmoris/ImRex">https://github.com/pmoris/ImRex</a>).</strong></p> <p><strong>Overview of different experiments:</strong></p> <ul> <li>Two main architectures were compared: the interaction map (or <code>padded</code>) CNN and a dual input CNN based on NetTCR (<code>nettcr</code>).</li> <li>Two different cross-validation strategies were used: a 5x repeated 5-fold CV (<code>repeated5fold</code>) and an epitope-grouped CV (<code>epitope_grouped</code>).</li> <li>The different dataset subsets are labelled as follows. Check the Makefile's <code>preprocess-vdjdb-aug-2019</code> command (and the underlying script <code>./src/scripts/preprocessing/preprocess_vdjdb.py</code>) for a more thorough overview of the different filtering options. <ul> <li><code>mhci</code>: only MHCI class presented epitopes.</li> <li><code>trb</code>: only TRB CDR3 sequences.</li> <li><code>tra</code>: only TRA CDR3 sequences.</li> <li><code>tratrb</code>: both types of CDR3 sequences.</li> <li><code>down</code>: moderate downsampling of most abundant epitopes to 1000 pairs.</li> <li><code>down400</code>: strong downsampling of most abundant epitopes to 400 pairs.</li> <li><code>decoy</code>: decoy epitope data.</li> <li><code>reg001</code>: regularization factor 0.01 (only for padded/interaction type models, fixed value)</li> </ul> </li> <li>Two different methods of generating negative TCR-epitope pairs were used: shuffling of positive pairs, i.e. sampling a single epitope from the positive pairs for each CDR3 sequence (<code>shuffle</code>), and sampling CDR3s from a reference repertoire (<code>negref</code>).</li> <li>The batch size is labelled as <code>b32</code> = a batch size of 32.</li> <li>The learning rate was always 0.0001 (<code>lre4</code>) or 0.001 (<code>lre3</code>).</li> </ul> <p> </p>
Datasets associated with the manuscript "Discovering SARS-CoV-2 neoepitopes and the associated TCR-pMHC recognition mechanisms by combining single-cell sequencing, deep learning, and molecular dynamics simulation techniques"
<p>meta_data_TCR-pMHC_from_STCRDab.tsv, TCR-pMHC structures used for contacts analysis.</p><p>tcr_gliph_input_sars2.tsv, input files (TCR sequences and related information) used for clustering TCRs targeting SARS-CoV-2 epitopes and epitope-unknown TCRs.</p><p>tcr_gliph_input_non-sars2.tsv, input files used for clustering TCRs targeting non-SARS-CoV-2 epitopes and epitope-unknown TCRs.</p><p>tcr_gliph_output*, output files from the GLIPH software, including the recognized TCR clusters by GLIPH (convergence-group.txt), the linkage information of TCR clusters (clone-network.txt), and the recognized motif in TCR clusters (kmer.txt).</p><p>md_trajs.tar, structures and MD simulation trajectories of TCR-614-pMHC and TCR-204-pMHC complexes.</p>
About epiTCR-KDA: Knowledge Distillation model on Dihedral Angles for TCR-peptide prediction
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Allen Brain Atlas
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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.