AI Medical Compendium Topic

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Receptors, Antigen, T-Cell

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Training of epitope-TCR prediction models with healthy donor-derived cancer-specific T cells.

Methods in cell biology
Discovery of epitope-specific T-cell receptors (TCRs) for cancer therapies is a time consuming and expensive procedure that usually requires a large amount of patient cells. To maximize information from and minimize the need of precious samples in ca...

Applying T-classifier, binary classifiers, upon high-throughput TCR sequencing output to identify cytomegalovirus exposure history.

Scientific reports
With the continuous development of information technology and the running speed of computers, the development of informatization has led to the generation of increasingly more medical data. Solving unmet needs such as employing the constantly develop...

ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins.

Communications biology
Immune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these proteins is critical for understanding their antigen binding properties. Here, we present ImmuneBuilder, a set of de...

Tri-objective optimization of a waste-to-energy plant with super critical carbon dioxide and multi-effect water desalination for building application based on biomass fuels.

Chemosphere
In the present research, an innovative biomass-based energy system for the production of electricity and desalinated water for building application is proposed. The main subsystems of this power plant include gasification cycle, gas turbine (GT), sup...

MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction.

Briefings in bioinformatics
Classifying epitopes is essential since they can be applied in various fields, including therapeutics, diagnostics and peptide-based vaccines. To determine the epitope or peptide against an antibody, epitope mapping with peptides is the most extensiv...

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

Nucleic acids research
The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the...

DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis.

Science advances
Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for pr...

TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning.

Protein science : a publication of the Protein Society
The recognition of T-cell receptor (TCR) on the surface of T cell to specific epitope presented by the major histocompatibility complex is the key to trigger the immune response. Identifying the binding rules of TCR-epitope pair is crucial for develo...

HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction.

Communications biology
Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, w...

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...