Patents by Inventor YUQIANG HAN

YUQIANG HAN has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • PERCEPTUAL REPRESENTATION LEARNING METHOD FOR PROTEIN CONFORMATIONS BASED ON PRE-TRAINED LANGUAGE MODEL
    Publication number: 20240233875
    Abstract: The present invention discloses a perceptual representation learning method for protein conformations based on a pre-trained language model, including: obtaining a protein made up of an amino acid sequence, building different data sets according to protein conformations, and defining a prompt for each type of protein conformation; building, based on a pre-trained language model, a representation learning module for fusing an embedding representation of each type of the prompt into an embedding representation of the protein, so as to obtain a protein embedding representation under a prompt identifier; building a task module for performing task prediction on a task corresponding to each type of protein conformation based on the protein embedding representation under the prompt identifier; building a loss function for each type of task based on a task prediction result and a tag, and updating model parameters of the representation learning module and the task module in combination with loss functions of all type
    Type: Application
    Filed: October 21, 2022
    Publication date: July 11, 2024
    Inventors: QIANG ZHANG, ZEYUAN WANG, YUQIANG HAN, HUAJUN CHEN
  • PERCEPTUAL REPRESENTATION LEARNING METHOD FOR PROTEIN CONFORMATIONS BASED ON PRE-TRAINED LANGUAGE MODEL
    Publication number: 20240136021
    Abstract: The present invention discloses a perceptual representation learning method for protein conformations based on a pre-trained language model, including: obtaining a protein made up of an amino acid sequence, building different data sets according to protein conformations, and defining a prompt for each type of protein conformation; building, based on a pre-trained language model, a representation learning module for fusing an embedding representation of each type of the prompt into an embedding representation of the protein, so as to obtain a protein embedding representation under a prompt identifier; building a task module for performing task prediction on a task corresponding to each type of protein conformation based on the protein embedding representation under the prompt identifier; building a loss function for each type of task based on a task prediction result and a tag, and updating model parameters of the representation learning module and the task module in combination with loss functions of all type
    Type: Application
    Filed: October 20, 2022
    Publication date: April 25, 2024
    Inventors: QIANG ZHANG, ZEYUAN WANG, YUQIANG HAN, HUAJUN CHEN