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    High-order semi-Restricted Boltzmann Machines and Deep Models for accurate peptide-MHC binding prediction
    62.
    发明申请
    High-order semi-Restricted Boltzmann Machines and Deep Models for accurate peptide-MHC binding prediction 审中-公开
    高阶半限制Boltzmann机器和深度模型用于准确的肽-MHC结合预测

    公开(公告)号：US20150278441A1

    公开(公告)日：2015-10-01

    申请号：US14512332

    申请日：2014-10-10

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Pavel Kuksa ,  Xia Ning

    IPC: G06F19/24 ,  G06N3/08

    CPC classification number: G16B40/00 ,  G06N20/00 ,  G16B20/00

    Abstract: A method for peptide binding prediction includes receiving a peptide sequence descriptor and descriptors of contacting amino acids on major histocompatibility complex (MHC) protein-peptide interaction structure; generating a model with an ensemble of high order neural network; pre-training the model by high order semi-restricted Boltzmann machine (RBM) or high-order denoising autoencoder; and generating a prediction as a binary output or continuous output with initial model parameters pre-trained using binary output data if available. A systematic learning method for leveraging high-order interactions/associations among items for better collaborative filtering and item recommendation.

    Abstract translation: 肽结合预测的方法包括接收肽序列描述符和在主要组织相容性复合体（MHC）蛋白 - 肽相互作用结构上接触氨基酸的描述符; 产生具有高阶神经网络集合的模型; 使用高阶半限制玻尔兹曼（RBM）或高阶去噪自动编码器对该模型进行预训练; 并且如果可用，则使用二进制输出数据预训练的初始模型参数生成作为二进制输出或连续输出的预测。 一种系统的学习方法，用于利用项目之间的高阶交互/关联，以实现更好的协同过滤和项目推荐。

    DISENTANGLED WASSERSTEIN AUTOENCODER FOR PROTEIN ENGINEERING
    64.
    发明公开
    DISENTANGLED WASSERSTEIN AUTOENCODER FOR PROTEIN ENGINEERING 审中-公开

    公开(公告)号：US20240078430A1

    公开(公告)日：2024-03-07

    申请号：US18449748

    申请日：2023-08-15

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Hans Peter Graf ,  Tianxiao Li

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: A computer-implemented method for learning disentangled representations for T-cell receptors to improve immunotherapy is provided. The method includes optionally introducing a minimal number of mutations to a T-cell receptor (TCR) sequence to enable the TCR sequence to bind to a peptide, using a disentangled Wasserstein autoencoder to separate an embedding space of the TCR sequence into functional embeddings and structural embeddings, feeding the functional embeddings and the structural embeddings to a long short-term memory (LSTM) or transformer decoder, using an auxiliary classifier to predict a probability of a positive binding label from the functional embeddings and the peptide, and generating new TCR sequences with enhanced binding affinity for immunotherapy to target a particular virus or tumor.

    PEPTIDE SEARCH SYSTEM FOR IMMUNOTHERAPY
    65.
    发明公开
    PEPTIDE SEARCH SYSTEM FOR IMMUNOTHERAPY 审中-公开

    公开(公告)号：US20240071571A1

    公开(公告)日：2024-02-29

    申请号：US18471641

    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.

    TCR ENGINEERING WITH DEEP REINFORCEMENT LEARNING FOR INCREASING EFFICACY AND SAFETY OF TCR-T IMMUNOTHERAPY
    66.
    发明公开
    TCR ENGINEERING WITH DEEP REINFORCEMENT LEARNING FOR INCREASING EFFICACY AND SAFETY OF TCR-T IMMUNOTHERAPY 审中-公开

    公开(公告)号：US20230304189A1

    公开(公告)日：2023-09-28

    申请号：US18174799

    申请日：2023-02-27

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Hans Peter Graf

    IPC: C40B30/04 ,  G06N3/092 ,  G06N3/0442 ,  C40B20/04 ,  G16B40/00

    CPC classification number: C40B30/04 ,  G06N3/092 ,  G06N3/0442 ,  C40B20/04 ,  G16B40/00

    Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs for immunotherapy includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from patients, predicting interaction scores between the extracted peptides and the TCRs from the patients, developing a deep reinforcement learning framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions, outputting mutated TCRs, ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells, and for each top-ranked TCR candidate, repeatedly identifying a set of self-peptides that the top-ranked TCR candidate binds to and further optimizing it greedily by maximizing a sum of its interaction scores with a given set of peptide antigens while minimizing a sum of its interaction scores with the set of self-peptides until stopping criteria of efficacy and safety are met.

    T-CELL RECEPTOR OPTIMIZATION WITH REINFORCEMENT LEARNING AND MUTATION POLICIES FOR PRECISION IMMUNOTHERAPY
    68.
    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：US20230253068A1

    公开(公告)日：2023-08-10

    申请号：US18151686

    申请日：2023-01-09

    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-BASED VACCINE GENERATION SYSTEM
    69.
    发明申请
    PEPTIDE-BASED VACCINE GENERATION SYSTEM 有权

    公开(公告)号：US20210319847A1

    公开(公告)日：2021-10-14

    申请号：US17197166

    申请日：2021-03-10

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Wenchao Yu ,  Hans Peter Graf ,  Igor Durdanovic

    IPC: G16B15/30 ,  G16B40/00 ,  G06N3/08 ,  G06N3/04

    Abstract: A method is provided for peptide-based vaccine generation. The method receives a dataset of positive and negative binding peptide sequences. The method pre-trains a set of peptide binding property predictors on the dataset to generate training data. The method trains a Wasserstein Generative Adversarial Network (WGAN) only on the positive binding peptide sequences, in which a discriminator of the WGAN is updated to distinguish generated peptide sequences from sampled positive peptide sequences from the training data, and a generator of the WGAN is updated to fool the discriminator. The method trains the WGAN only on the positive binding peptide sequences while simultaneously updating the generator to minimize a kernel Maximum Mean Discrepancy (MMD) loss between the generated peptide sequences and the sampled peptide sequences and maximize prediction accuracies of a set of pre-trained peptide binding property predictors with parameters of the set of pre-trained peptide binding property predictors being fixed.

    Context-aware attention-based neural network for interactive question answering
    70.
    发明授权
    Context-aware attention-based neural network for interactive question answering 有权

    公开(公告)号：US11087199B2

    公开(公告)日：2021-08-10

    申请号：US15789614

    申请日：2017-10-20

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Asim Kadav ,  Huayu Li

    IPC: G06N3/04 ,  G06N5/04 ,  G06N3/00

    Abstract: A context-aware attention-based neural network is provided for answering an input question given a set of purportedly supporting statements for the input question. The neural network includes a processing element. The processing element is configured to calculate a question representation for the input question, based on word annotations and word-level attentions calculated for the input question. The processing element is further configured to calculate a sentence representation for each of the purportedly supporting statements, based on word annotations and word-level attentions calculated for each of the purportedly supporting statements. The processing element is also configured to calculate a context representation for the set of purportedly supporting statements with respect to the sentence representation for each of the purportedly supporting statements. The processing element is additionally configured to generate an answer to the input question based on the question representation and the context representation.

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