中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

109 records (took 0.02 seconds)

    STRUCTURE LEARNING IN GNNS FOR MEDICAL DECISION MAKING USING TASK-RELEVANT GRAPH REFINEMENT
    2.
    发明申请
    STRUCTURE LEARNING IN GNNS FOR MEDICAL DECISION MAKING USING TASK-RELEVANT GRAPH REFINEMENT 有权

    公开(公告)号：US20240386266A1

    公开(公告)日：2024-11-21

    申请号：US18666088

    申请日：2024-05-16

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jonathan Warrell ,  Eric Cosatto ,  Renqiang Min ,  Tianci Song

    IPC: G06N3/08

    Abstract: A method for graph analysis includes identifying trainable control parameters of a graph refinement function. Sample graph refinements of an input graph are generated, using control parameters sampled from a variational distribution. Graph refinement control parameters associated with a sample graph refinement that has a highest performance score are selected when used to train a graph neural network. Graph analysis is performed on the input graph using the selected graph refinement parameters to produce a refined graph on new test samples. An action is performed responsive to the graph analysis.

    T-CELL RECEPTOR OPTIMIZATION WITH REINFORCEMENT LEARNING AND MUTATION POLICIES FOR PRECISION IMMUNOTHERAPY
    3.
    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
    5.
    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
    7.
    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.

    Mobile phone with system failure prediction using long short-term memory neural networks
    10.
    发明授权
    Mobile phone with system failure prediction using long short-term memory neural networks 有权

    公开(公告)号：US10296430B2

    公开(公告)日：2019-05-21

    申请号：US15478753

    申请日：2017-04-04

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jianwu Xu ,  Ke Zhang ,  Hui Zhang ,  Renqiang Min ,  Guofei Jiang

    IPC: G06F11/22 ,  G06F11/00 ,  G06N3/08

    Abstract: Mobile phones and methods for mobile phone failure prediction include receiving respective log files from one or more mobile phone components, including at least one user application. The log files have heterogeneous formats. A likelihood of failure of one or more mobile phone components is determined based on the received log files by clustering the plurality of log files according to structural log patterns and determining feature representations of the log files based on the log clusters. A user is alerted to a potential failure if the likelihood of component failure exceeds a first threshold. An automatic system control action is performed if the likelihood of component failure exceeds a second threshold.

Patent Agency Ranking