AIMC Topic: T-Lymphocytes
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Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.
Scientific reports
Sep 30, 2021
Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. ...
Development of quantitative model of a local lymph node assay for evaluating skin sensitization potency applying machine learning CatBoost.
Regulatory toxicology and pharmacology : RTP
Jul 24, 2021
The estimated concentrations for a stimulation index of 3 (EC3) in murine local lymph node assay (LLNA) is an important quantitative value for determining the strength of skin sensitization to chemicals, including cosmetic ingredients. However, anima...
Structural constraints in T-cell repertoire selection predicted by machine learning.
Genes and immunity
Jul 12, 2021
The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning.
Frontiers in immunology
Jun 28, 2021
BACKGROUND: Gastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies.
Ligand Nanocluster Array Enables Artificial-Intelligence-Based Detection of Hidden Features in T-Cell Architecture.
Nano letters
Jun 25, 2021
Protein patterning has emerged as a powerful means to interrogate adhering cells. However, the tools to apply a sub-micrometer periodic stimulus and the analysis of the response are still being standardized. We propose a technique combining electron ...
Applications of Machine and Deep Learning in Adaptive Immunity.
Annual review of chemical and biomolecular engineering
Apr 14, 2021
Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs)...
DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.
Nature communications
Mar 11, 2021
Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diver...
Role of Soluble T-Cell Immunoglobulin Mucin Domain-3 in Differentiating Nontuberculous Mycobacterial Lung Disease from Pulmonary Colonization.
Archivos de bronconeumologia
Feb 24, 2021
BACKGROUND: Differentiating between nontuberculous mycobacterial lung disease (NTM-LD) and pulmonary NTM colonization (NTM-Col) is difficult. Compared with healthy controls, patients with NTM-LD generally present immune tolerance along with increased...
TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties.
Computational biology and chemistry
Feb 8, 2021
Immunotherapy is a research area with great potential in drug discovery for cancer treatment. Because of the capacity of tumor antigens to activate the immune response and promote the destruction of tumor cells, they are considered excellent immunoth...
Use of machine learning to identify a T cell response to SARS-CoV-2.
Cell reports. Medicine
Jan 16, 2021
The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease ...
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