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T-Lymphocytes

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The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning.

Frontiers in immunology
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.

TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties.

Computational biology and chemistry
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...

DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Nature communications
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...

Applications of Machine and Deep Learning in Adaptive Immunity.

Annual review of chemical and biomolecular engineering
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)...

Deep generative selection models of T and B cell receptor repertoires with soNNia.

Proceedings of the National Academy of Sciences of the United States of America
Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B cell lymphocytes rely on the diversity of their receptor chains to recogn...

Ligand Nanocluster Array Enables Artificial-Intelligence-Based Detection of Hidden Features in T-Cell Architecture.

Nano letters
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 ...

Development of quantitative model of a local lymph node assay for evaluating skin sensitization potency applying machine learning CatBoost.

Regulatory toxicology and pharmacology : RTP
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...

Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.

Scientific reports
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. ...

XGSEA: CROSS-species gene set enrichment analysis via domain adaptation.

Briefings in bioinformatics
MOTIVATION: Gene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. Howeve...