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    T-CELL RECEPTOR OPTIMIZATION WITH REINFORCEMENT LEARNING AND MUTATION POLICIES FOR PRECISION IMMUNOTHERAPY
    13.
    发明公开
    T-CELL RECEPTOR OPTIMIZATION WITH REINFORCEMENT LEARNING AND MUTATION POLICIES FOR PRECISION IMMUNOTHERAPY 审中-公开

    公开(公告)号：US20240177799A1

    公开(公告)日：2024-05-30

    申请号：US18414687

    申请日：2024-01-17

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Hans Peter Graf ,  Ziqi Chen

    IPC: G16B15/30 ,  G06N20/00 ,  G16B20/50 ,  G16B40/20

    CPC classification number: G16B15/30 ,  G06N20/00 ,  G16B20/50 ,  G16B40/20

    Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy is presented. The method includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from target patients, predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients, developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions based on a reconstruction-based score and a density estimation-based score, randomly sampling batches of TCRs and following a policy network to mutate the TCRs, outputting mutated TCRs, and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.

    PEPTIDE SEARCH SYSTEM FOR IMMUNOTHERAPY
    15.
    发明公开
    PEPTIDE SEARCH SYSTEM FOR IMMUNOTHERAPY 审中-公开

    公开(公告)号：US20240071570A1

    公开(公告)日：2024-02-29

    申请号：US18471630

    申请日：2023-09-21

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Hans Peter Graf ,  Ziqi Chen

    IPC: G16B40/00 ,  G06N3/08 ,  G16B15/30

    CPC classification number: G16B40/00 ,  G06N3/08 ,  G16B15/30

    Abstract: A system for binding peptide search for immunotherapy is presented. The system includes employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex allele sequences and peptide sequences, training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences into continuous embedding vectors, running a Monte Carlo Tree Search to generate a first set of positive peptide vaccine candidates, running a Bayesian Optimization search with the trained VAE and a Backpropagation search with the trained VAE to generate a second set of positive peptide vaccine candidates, using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates, screening and merging the first, second, and third sets of positive peptide vaccine candidates, and outputting qualified peptides for immunotherapy from the screened and merged sets of positive peptide vaccine candidates.

    PEPTIDE SEARCH SYSTEM FOR IMMUNOTHERAPY
    17.
    发明申请
    PEPTIDE SEARCH SYSTEM FOR IMMUNOTHERAPY 有权

    公开(公告)号：US20230083313A1

    公开(公告)日：2023-03-16

    申请号：US17898662

    申请日：2022-08-30

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Hans Peter Graf ,  Ziqi Chen

    IPC: G16B40/00 ,  G16B15/30 ,  G06N3/08

    Abstract: A system for binding peptide search for immunotherapy is presented. The system includes employing a deep neural network to predict a peptide presentation given Major Histocompatibility Complex allele sequences and peptide sequences, training a Variational Autoencoder (VAE) to reconstruct peptides by converting the peptide sequences into continuous embedding vectors, running a Monte Carlo Tree Search to generate a first set of positive peptide vaccine candidates, running a Bayesian Optimization search with the trained VAE and a Backpropagation search with the trained VAE to generate a second set of positive peptide vaccine candidates, using a sampling from a Position Weight Matrix (sPWM) to generate a third set of positive peptide vaccine candidates, screening and merging the first, second, and third sets of positive peptide vaccine candidates, and outputting qualified peptides for immunotherapy from the screened and merged sets of positive peptide vaccine candidates.

    Keypoint based pose-tracking using entailment
    18.
    发明授权
    Keypoint based pose-tracking using entailment 有权

    公开(公告)号：US11475590B2

    公开(公告)日：2022-10-18

    申请号：US17016273

    申请日：2020-09-09

    Applicant: NEC Laboratories America, Inc.

    Inventor： Asim Kadav ,  Farley Lai ,  Hans Peter Graf ,  Michael Snower

    IPC: G06T7/73

    Abstract: Aspects of the present disclosure describe systems, methods and structures for an efficient multi-person posetracking method that advantageously achieves state-of-the-art performance on PoseTrack datasets by only using keypoint information in a tracking step without optical flow or convolution routines. As a consequence, our method has fewer parameters and FLOPs and achieves faster FPS. Our method benefits from our parameter-free tracking method that outperforms commonly used bounding box propagation in top-down methods. Finally, we disclose tokenization and embedding multi-person pose keypoint information in the transformer architecture that can be re-used for other pose tasks such as pose-based action recognition.

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