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    TCR-REPERTOIRE FRAMEWORK FOR MULTIPLE DISEASE DIAGNOSIS
    286.
    发明公开
    TCR-REPERTOIRE FRAMEWORK FOR MULTIPLE DISEASE DIAGNOSIS 审中-公开

    公开(公告)号：US20240290418A1

    公开(公告)日：2024-08-29

    申请号：US18571515

    申请日：2022-06-17

    Applicant: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM

    Inventor： Bo LI

    IPC: G16B15/00 ,  G16B40/20 ,  G16H50/20

    CPC classification number: G16B15/00 ,  G16B40/20 ,  G16H50/20

    Abstract: A novel method of geometric isometry based antigen-specific TCR alignment (GIANA) is described herein. GIANA is an antigen-specific TCR clustering method that is able to efficiently handle tens of millions of sequences. GIANA achieved higher sensitivity and precision than all existing methods, and is able to retrieve TCRs specific to known antigens with high accuracy. The ultra-large-scale TCR clustering and fast query of novel samples also enabled a novel reference-based repertoire classification framework. GIANA can also analyze single cell RNA-seq data with TCR regions solved, and it is possible to query TCRs from unknown data against the large database of TCR repertoire samples in the public domain, and provide new insights over shared antigen-specificity. GIANA is applicable to cluster or query large B cell receptor sequencing data as well.

    HYBRID SEQUENCE-STRUCTURE DEEP LEARNING SYSTEM FOR PREDICTING THE T CELL RECEPTOR BINDING SPECIFICITY OF T CELL ANTIGENS
    288.
    发明公开
    HYBRID SEQUENCE-STRUCTURE DEEP LEARNING SYSTEM FOR PREDICTING THE T CELL RECEPTOR BINDING SPECIFICITY OF T CELL ANTIGENS 审中-公开

    公开(公告)号：US20240282409A1

    公开(公告)日：2024-08-22

    申请号：US18650820

    申请日：2024-04-30

    Applicant: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM

    Inventor： Tao WANG ,  Yi Han ,  Tianshi Lu ,  Yuqiu Yang

    IPC: G16B20/30 ,  G16B25/10 ,  G16B40/00

    CPC classification number: G16B20/30 ,  G16B25/10 ,  G16B40/00

    Abstract: The disclosed technology relates to a computer-implemented method for predicting T cell receptor (TCR) binding specificities towards T cell antigen targets (namely, peptide-major histocompatibility complexes, pMHCs), and a set of extensions of this method, include prediction of immune-related adverse events (irAEs) using a machine learning model. The method involves obtaining genomic and proteomic data from patients, determining TCR and pMHC sequences by analyzing these data, and predicting binding interactions between T cell antigens and the TCRs. The extensions include: (a) a transfer learning model for improving the predictive performance of a pre-trained TCR-antigen binding model as a foundation model, to enhance prediction for a specific pMHC, (b) a biomarker metric defined based on the output of the TCR-pMHC binding prediction method, for diagnosis, prognosis and response prediction purposes, (c) a method, based on the output of the TCR-pMHC binding prediction method, to select optimal antigens for tumor vaccines.

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