AIMC Topic: Receptors, Antigen, T-Cell

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Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences.

BMC bioinformatics
Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to ...

Machine Learning Approaches to TCR Repertoire Analysis.

Frontiers in immunology
Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning...

CD147-specific chimeric antigen receptor T cells effectively inhibit T cell acute lymphoblastic leukemia.

Cancer letters
T cell acute lymphoblastic leukemia (T-ALL) is invasive and heterogeneous, and existing therapies are sometimes unsuccessful. Chimeric antigen receptor (CAR) T cell therapy is a breakthrough tumor treatment method, particularly for B cell acute lymph...

A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires.

Journal of immunology (Baltimore, Md. : 1950)
The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immune responses and the potential for future immunity. Analyses of TCR repertoires via deep learning (DL...

T Cell Epitope Prediction and Its Application to Immunotherapy.

Frontiers in immunology
T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for ep...

NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data.

Communications biology
Prediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly challenging. This challenge is primarily due to three dominant factors: data accuracy, data scarceness, and problem complexity. Here, we showcase that "shallow...

A structural-based machine learning method to classify binding affinities between TCR and peptide-MHC complexes.

Molecular immunology
The activation of T cells is triggered by the interactions of T cell receptors (TCRs) with their epitopes, which are peptides presented by major histocompatibility complex (MHC) on the surfaces of antigen presenting cells (APC). While each TCR can on...

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)...

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...

Use of machine learning to identify a T cell response to SARS-CoV-2.

Cell reports. Medicine
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 ...