AIMC Topic: T-Lymphocytes

Clear Filters Showing 41 to 50 of 76 articles

A Machine Learning Approach Yields a Multiparameter Prognostic Marker in Liver Cancer.

Cancer immunology research Jan 11, 2021
A number of staging systems have been developed to predict clinical outcomes in hepatocellular carcinoma (HCC). However, no general consensus has been reached regarding the optimal model. New approaches such as machine learning (ML) strategies are po...
Venous Thrombosis Neoplasm Invasiveness Liver Adolescent Risk Factors Liver Neoplasms Young Adult Carcinoma, Hepatocellular Adult Lymphocyte Count Middle Aged Kaplan-Meier Estimate Risk Assessment Models, Immunological T-Lymphocytes Immune Checkpoint Proteins Male Neoplasm Staging Prognosis Hepatectomy Retrospective Studies Portal Vein Humans Female Machine Learning
View on PubMed DOI

Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells.

eLife Dec 17, 2020
The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniq...
T-Lymphocytes Deep Learning K562 Cells Tomography, Optical Humans Receptors, Chimeric Antigen Immunological Synapses
View on PubMed DOI

Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs.

Frontiers in immunology Aug 25, 2020
Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents too...
Binding Sites Protein Interaction Domains and Motifs Databases, Protein Ligands Antigens Receptors, Antigen, T-Cell Protein Binding Signal Transduction Humans Software Peptides T-Lymphocytes Deep Learning Epitopes, T-Lymphocyte
View on PubMed DOI

A robust and interpretable end-to-end deep learning model for cytometry data.

Proceedings of the National Academy of Sciences of the United States of America Aug 14, 2020
Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated ...
T-Lymphocytes Deep Learning Flow Cytometry Humans Cytomegalovirus Infections
View on PubMed DOI

iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation.

Analytical biochemistry Apr 22, 2020
In spite of the repertoire of existing cancer therapies, the ongoing recurrence and new cases of cancer poses a challenging health concern that prompts for novel and effective treatment. Cancer immunotherapy represents a promising venue for treatment...
Antigens, Neoplasm Histocompatibility Antigens Class I Humans Neoplasms Immunotherapy Machine Learning T-Lymphocytes
View on PubMed DOI

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks.

Journal of biophotonics Dec 15, 2019
The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired...
Image Processing, Computer-Assisted Machine Learning Humans Software T-Lymphocytes Neural Networks, Computer
View on PubMed DOI

Zoster patients on earth and astronauts in space share similar immunologic profiles.

Life sciences in space research Oct 14, 2019
BACKGROUND: On long-duration spaceflight, most astronauts experience persistent immune dysregulation and the reactivation of latent herpesviruses, including varicella zoster virus (VZV). To understand the clinical risk of these perturbations to astro...
Male Aged Humans Herpes Zoster Adult DNA, Viral Cytokines Middle Aged Herpesvirus 3, Human T-Lymphocytes Saliva Female Astronauts Lymphocyte Activation
View on PubMed DOI

Exploring single-cell data with deep multitasking neural networks.

Nature methods Oct 7, 2019
It is currently challenging to analyze single-cell data consisting of many cells and samples, and to address variations arising from batch effects and different sample preparations. For this purpose, we present SAUCIE, a deep neural network that comb...
Humans Single-Cell Analysis Cluster Analysis Neural Networks, Computer Dengue T-Lymphocytes
View on PubMed DOI

BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes.

Genome biology Aug 12, 2019
To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for identifying cell lineages and bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch Effect ReMoval...
Sequence Analysis, RNA Humans Pancreas Gene Expression Profiling Deep Learning Single-Cell Analysis Algorithms T-Lymphocytes Leukocytes, Mononuclear
View on PubMed DOI

Cellular frustration algorithms for anomaly detection applications.

PloS one Jul 8, 2019
Cellular frustrated models have been developed to describe how the adaptive immune system works. They are composed by independent agents that continuously pair and unpair depending on the information that one sub-set of these agents display. The emer...
Artificial Intelligence Algorithms T-Lymphocytes Data Mining Adaptive Immunity Humans Support Vector Machine Immune System Models, Statistical Models, Biological
View on PubMed DOI
Popular Topics
Recent Journals