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    PEPTIDE SEARCH SYSTEM FOR IMMUNOTHERAPY
    2.
    发明公开
    PEPTIDE SEARCH SYSTEM FOR IMMUNOTHERAPY 审中-公开

    公开(公告)号：US20240071572A1

    公开(公告)日：2024-02-29

    申请号：US18471667

    申请日：2023-09-21

    申请人： NEC Laboratories America, Inc.

    发明人： Renqiang Min ,  Hans Peter Graf ,  Ziqi Chen

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

    CPC分类号： G16B40/00 ,  G06N3/08 ,  G16B15/30

    摘要： 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 MUTATION POLICIES FOR TARGETED IMMUNOTHERAPY
    4.
    发明申请
    PEPTIDE MUTATION POLICIES FOR TARGETED IMMUNOTHERAPY 有权

    公开(公告)号：US20220327425A1

    公开(公告)日：2022-10-13

    申请号：US17711658

    申请日：2022-04-01

    申请人： NEC Laboratories America, Inc.

    发明人： Renqiang Min ,  Hans Peter Graf ,  Ligong Han

    IPC分类号： G06N20/00

    摘要： Methods and systems for training a machine learning model include embedding a state, including a peptide sequence and a protein, as a vector. An action, including a modification to an amino acid in the peptide sequence, is predicted using a presentation score of the peptide sequence by the protein as a reward. A mutation policy model is trained, using the state and the reward, to generate modifications that increase the presentation score.

    Detecting dangerous driving situations by parsing a scene graph of radar detections
    5.
    发明授权
    Detecting dangerous driving situations by parsing a scene graph of radar detections 有权

    公开(公告)号：US11055605B2

    公开(公告)日：2021-07-06

    申请号：US15785796

    申请日：2017-10-17

    申请人： NEC Laboratories America, Inc.

    发明人： Hans Peter Graf ,  Eric Cosatto ,  Iain Melvin

    IPC分类号： G06N3/08 ,  G06N3/04 ,  G01S7/41 ,  G01S13/931

    摘要： A computer-implemented method executed by a processor for training a neural network to recognize driving scenes from sensor data received from vehicle radar is presented. The computer-implemented method includes extracting substructures from the sensor data received from the vehicle radar to define a graph having a plurality of nodes and a plurality of edges, constructing a neural network for each extracted substructure, combining the outputs of each of the constructed neural networks for each of the plurality of edges into a single vector describing a driving scene of a vehicle, and classifying the single vector into a set of one or more dangerous situations involving the vehicle.

    NETWORK REPARAMETERIZATION FOR NEW CLASS CATEGORIZATION
    6.
    发明申请
    NETWORK REPARAMETERIZATION FOR NEW CLASS CATEGORIZATION 审中-公开

    公开(公告)号：US20200097757A1

    公开(公告)日：2020-03-26

    申请号：US16580199

    申请日：2019-09-24

    申请人： NEC Laboratories America, Inc.

    发明人： Renqiang Min ,  Kai Li ,  Bing Bai ,  Hans Peter Graf

    IPC分类号： G06K9/62 ,  G06K9/46 ,  G06N3/08

    摘要： A computer-implemented method and system are provided for training a model for New Class Categorization (NCC) of a test image. The method includes decoupling, by a hardware processor, a feature extraction part from a classifier part of a deep classification model by reparametrizing learnable weight variables of the classifier part as a combination of learnable variables of the feature extraction part and of a classification weight generator of the classifier part. The method further includes training, by the hardware processor, the deep classification model to obtain a trained deep classification model by (i) learning the feature extraction part as a multiclass classification task, and (ii) episodically training the classifier part by learning a classification weight generator which outputs classification weights given a training image.