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T-Lymphocytes

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iTTCA-MVL: A multi-view learning model based on physicochemical information and sequence statistical information for tumor T cell antigens identification.

Computers in biology and medicine
Immunotherapy is an emerging treatment method aimed at activating the human immune system and relying on its own immune function to kill cancer cells and tumor tissues. It has the advantages of wide applicability and minimal side effects. Effective i...

Inference of Developmental Hierarchy and Functional States of Exhausted T Cells from Epigenetic Profiles with Deep Learning.

Journal of chemical information and modeling
Exhausted T cells are a key component of immune cells that play a crucial role in the immune response against cancer and influence the efficacy of immunotherapy. Accurate assessment and measurement of T-cell exhaustion (TEX) are critical for understa...

An integrative machine learning model for the identification of tumor T-cell antigens.

Bio Systems
The escalating global incidence of cancer poses significant health challenges, underscoring the need for innovative and more efficacious treatments. Cancer immunotherapy, a promising approach leveraging the body's immune system against cancer, emerge...

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.

Nature communications
T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specific...

Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma.

International immunopharmacology
BACKGROUND: Establishment of a reliable prognostic model and identification of novel biomarkers are urgently needed to develop precise therapy strategies for clear cell renal cell carcinoma (ccRCC). Stress response stated T cells (Tstr) are a new T-c...

Reinforcement learning-guided control strategies for CAR T-cell activation and expansion.

Biotechnology and bioengineering
Reinforcement learning (RL), a subset of machine learning (ML), could optimize and control biomanufacturing processes, such as improved production of therapeutic cells. Here, the process of CAR T-cell activation by antigen-presenting beads and their ...

Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning.

BMC cardiovascular disorders
BACKGROUND: T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been implicated in numerous pathological conditions, but its role in acute myocardial Infarction (AMI) remains largely unexplored. This research aims to i...

Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer.

Cancer immunology research
Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning (ML) to analyze a single-cell, spatial, and highly multipl...

The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood.

Cancer research
UNLABELLED: T cells recognize tumor antigens and initiate an anticancer immune response in the very early stages of tumor development, and the antigen specificity of T cells is determined by the T-cell receptor (TCR). Therefore, monitoring changes in...