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公开(公告)号：US20230004774A1
公开(公告)日：2023-01-05
申请号：US17578683
申请日：2022-01-19
Inventor： Weibin LI , Zhifan ZHU , Shikun FENG , Shiwei HUANG , Jingzhou HE
Abstract: The present disclosure provides a method and apparatus for generating a node representation, an electronic device and a readable storage medium, and relates to the field of deep learning technologies. The method for generating a node representation includes: acquiring a heterogeneous graph to be processed; performing a sampling operation in the heterogeneous graph to be processed according to a first meta path, so as to obtain at least one first walk path; obtaining an initial node representation of each node in the heterogeneous graph to be processed according to the at least one first walk path; and generating the final node representation of each node according to the initial node representation of each node and initial node representations of neighbor nodes of each node. With the present disclosure, accuracy of the generated node representation may be improved.
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公开(公告)号：US20250092387A1
公开(公告)日：2025-03-20
申请号：US18968907
申请日：2024-12-04
Inventor： Yang LIU , Xiaomin FANG , Jie GAO , Xiaonan ZHANG , Jingzhou HE
IPC: C12N15/10
Abstract: A method and an apparatus for optimizing an mRNA sequence, an mRNA molecule, a pharmaceutical composition, and a use thereof are provided. The disclosure relates to the technical field of artificial intelligence, specifically to technical fields such as biological computing. The method for optimizing the mRNA sequence include: obtaining a first mRNA sequence for synthesizing a protein of interest, where the first mRNA sequence includes a 5′ untranslated region sequence and a coding region sequence; and adjusting the 5′ untranslated region sequence and the coding region sequence with the goal of maximizing a first score of the first mRNA sequence, so as to obtain an optimized second mRNA sequence for synthesizing the protein of interest, where the first score reflects at least one of the following indicators of the first mRNA sequence: translation initiation efficiency, codon adaptation index, and minimum free energy.
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公开(公告)号：US20220129753A1
公开(公告)日：2022-04-28
申请号：US17572921
申请日：2022-01-11
Inventor： Yuxiang LU , Jiaxiang LIU , Xuyi CHEN , Shikun FENG , Shuohuan WANG , Yu SUN , Shiwei HUANG , Jingzhou HE
Abstract: A pre-training method of a neural network model, an electronic device, and a medium. The pre-training data is inputted to the initial neural network model, and the initial neural network model is pre-trained in the first training mode, in the first training mode, the plurality of hidden layers share one hidden layer parameter, and the loss value of the initial neural network model is obtained, if the loss value of the initial neural network model is less than a preset threshold, the initial neural network model continues to be pre-trained in the second training mode, in the second training mode, each of the plurality of hidden layers has its own hidden layer parameter.
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公开(公告)号：US20230032324A1
公开(公告)日：2023-02-02
申请号：US17966127
申请日：2022-10-14
Inventor： Fan WANG , Hao TIAN , Haoyi XIONG , Hua WU , Jingzhou HE , Haifeng WANG
IPC: G06N20/00
Abstract: A method for training a decision-making model parameter, a decision determination method, an electronic device, and a non-transitory computer-readable storage medium are provided. In the method, a perturbation parameter is generated according to a meta-parameter, and first observation information of a primary training environment is acquired based on the perturbation parameter. According to the first observation information, an evaluation parameter of the perturbation parameter is determined. According to the perturbation parameter and the evaluation parameter thereof, an updated meta-parameter is generated. The updated meta-parameter is determined as a target meta-parameter, when it is determined, according to the meta-parameter and the updated meta-parameter, that a condition for stopping primary training is met. According to the target meta-parameter, a target memory parameter corresponding to a secondary training task is determined, where the target memory parameter and the target meta-parameter are used to make a decision corresponding to a prediction task.
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Patent Agency Ranking
公开(公告)号：US20220215899A1
公开(公告)日：2022-07-07
申请号：US17557691
申请日：2021-12-21
Inventor： Fan WANG , Jingzhou HE , Xiaomin FANG , Xiaonan ZHANG , Hua WU , Tian WU , Haifeng WANG
Abstract: The present disclosure discloses an affinity prediction method and apparatus, a method and apparatus for training an affinity prediction model, a device and a medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, smart medical technologies, or the like. An implementation includes: collecting a plurality of training samples, each training sample including information of a training target, information of a training drug and a test data set corresponding to the training target; and training an affinity prediction model using the plurality of training samples. In addition, there is further disclosed the affinity prediction method. The technology in the present disclosure may effectively improve accuracy and a training effect of the trained affinity prediction model. During an affinity prediction, accuracy of a predicted affinity of a target to be detected with a drug to be detected may be higher by acquiring a test data set corresponding to the target to be detected to participate in the prediction.
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公开(公告)号：US20230243661A1
公开(公告)日：2023-08-03
申请号：US17999917
申请日：2022-04-28
Inventor： Jizhou HUANG , Kejiao LI , Bo ZHOU , Fan WANG , Jingzhou HE , Haifeng WANG
CPC classification number: G01C21/3461 , G01C21/3492 , G08G1/0108
Abstract: Provided are a navigation path planning method and apparatus, a device, and a storage medium. The navigation path planning method includes planning at least two available navigation paths for each target user of at least two target users in a target region; and determining a global passing feature of the target region and selecting, according to the global passing feature of the target region, one available navigation path from the at least two available navigation paths corresponding to each target user to serve as a recommended navigation path to be recommended to each target user.
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公开(公告)号：US20230115984A1
公开(公告)日：2023-04-13
申请号：US18064812
申请日：2022-12-12
Inventor： Zhiyuan CHEN , Xiaomin FANG , Fan WANG , Jingzhou HE
Abstract: The present disclosure provides a method for training a model, a method and an apparatus for generating molecules, and relates to the technical field of computer technology, particularly the technical field of artificial intelligence. The particular implementation may include: obtaining first molecular samples and second molecular samples; determining molecular difference information based on the first molecular samples and the second molecular samples; training an initial encoding module and an initial generation module based on the molecular difference information to obtain a target encoding module and a target generation module; and determining a molecule generation model based on the target encoding module and the target generation module.
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公开(公告)号：US20220392585A1
公开(公告)日：2022-12-08
申请号：US17820688
申请日：2022-08-18
Inventor： Shanzhuo ZHANG , Lihang LIU , Yueyang HUANG , Donglong HE , Xiaomin FANG , Xiaonan ZHANG , Fan WANG , Jingzhou HE
Abstract: A method and apparatus for training a compound property prediction model, a device, a storage medium and a program product. A implementation of the method comprises: acquiring an unannotated compound data set; pre-training a graph neural network using the unannotated compound data set to obtain a pre-trained graph neural network; acquiring a plurality of annotated compound data sets, each annotated compound data set being annotated with one kind of compound property; and performing multi-task training on the pre-trained graph neural network using the plurality of annotated compound data sets, to obtain a compound property prediction model, the compound property prediction model being used to predict a plurality kinds of properties of a compound.
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公开(公告)号：US20220231504A1
公开(公告)日：2022-07-21
申请号：US17684131
申请日：2022-03-01
Inventor： Hongsheng ZENG , Bo ZHOU , Kejiao LI , Fan WANG , Yongfeng CHEN , Jingzhou HE
Abstract: A method for training a power system scheduling model includes: generating a plurality of first scheduling sub-models based on a first initial scheduling model; acquiring a first matching degree of historical running state information and each of candidate actions, output by each of the plurality of first scheduling sub-models, by inputting the historical running state information into each of the plurality of first scheduling sub-models; generating a second initial scheduling model by correcting the first initial scheduling model based on first matching degrees corresponding to each of the plurality of first scheduling sub-models; and returning to the generating the plurality of first scheduling sub-models based on the second initial scheduling model, until the matching degree output by the second initial scheduling module meets the convergence condition, determining the second initial scheduling model as the power system scheduling model.
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