Abstract: A method and device of similarity determination, network training, and search, an electronic device, and a storage medium are provided. The data similarity determination method includes: acquiring first data of a first object; mapping the first sub-data as a first semantic representation in a semantic comparison space, where the semantic comparison space enables a similarity between a semantic representation obtained by mapping data of the first modality to the semantic comparison space and a semantic representation obtained by mapping data of the second modality to the semantic comparison space to be computed; acquiring second data of a second object; mapping the second sub-data as a second semantic representation in the semantic comparison space; and calculating a similarity between the first data and the second data based on at least the first semantic representation and the second semantic representation.

Abstract: A method and device of similarity determination, network training, and search, an electronic device, and a storage medium are provided. The data similarity determination method includes: acquiring first data of a first object; mapping the first sub-data as a first semantic representation in a semantic comparison space, where the semantic comparison space enables a similarity between a semantic representation obtained by mapping data of the first modality to the semantic comparison space and a semantic representation obtained by mapping data of the second modality to the semantic comparison space to be computed; acquiring second data of a second object; mapping the second sub-data as a second semantic representation in the semantic comparison space; and calculating a similarity between the first data and the second data based on at least the first semantic representation and the second semantic representation.

Abstract: A method for predicting an RNA-protein interaction, relates to the technical field of artificial intelligence. The method includes: acquiring an RNA-protein pair to be predicted; obtaining a sequence feature of the RNA-protein pair to be predicted by performing feature extraction on the RNA-protein pair to be predicted; obtaining an RNA sequence representation vector and a protein sequence representation vector in the RNA-protein pair to be predicted by vectorizing the RNA-protein pair to be predicted; obtaining respectively by using multiple interaction prediction models, multiple interaction prediction values of the RNA-protein pair to be predicted, based on the sequence feature of the RNA-protein pair to be predicted, the RNA sequence representation vector and the protein sequence representation vector in the RNA-protein pair to be predicted; and determining an interaction between the RNA and the protein according to the multiple interaction prediction values.

Abstract: There are provided a method and an apparatus for distributing physical examination information, an electronic device, a computer-readable storage medium, and a computer program product. The method includes: obtaining physical examination information and information of a plurality of distribution objects; inputting the physical examination information and the information of the plurality of distribution objects into an information matching model obtained by pre-training to obtain a matching degree between the physical examination information and the plurality of distribution objects; and determining a target distribution object from the plurality of distribution objects according to the matching degree between the physical examination information and each of the plurality of distribution objects, and distributing the physical examination information to the target distribution object.

Abstract: Provided is a method for acquiring a target model, including: acquiring sample groups corresponding to sample users, the sample group including sample information of multiple modalities; performing iterative trainings on a preset model based on the sample groups to obtain the target model, the target model being configured to predict a prognosis evaluation value of a target object, in each iterative training, feature extraction is performed on the sample information of the multiple modalities in a current sample group using the preset model, a predicted prognosis evaluation value and a consistency expression value are determined based on extracted sample features, the consistency expression value is used for characterizing a consistency degree of the sample features corresponding to a same target disease; and parameters of the preset model are updated based on the predicted prognosis evaluation value, a prognosis evaluation label corresponding to the current sample group, the consistency expression value.

Abstract: The present disclosure provides Aa computer-implemented method, a method of training a deep learning model, an electronic device, and a medium are provided. The method includes: obtaining a target image segmentation result according to a target medical image of a target part, wherein the target medical image includes a medical image in at least one modality; obtaining target fusion data according to the target medical image segmentation result and a medical image in a predetermined modality in the target medical image; and obtaining a target multi-mutation detection result according to the target fusion data.

Abstract: An object information processing method includes: acquiring an image of an object to be detected; inputting the image into a joint model to obtain a plurality of labels of the object to be detected; and obtaining object information of the object to be detected based on the plurality of labels. The joint model includes a convolutional neural network, the convolutional neural network includes a plurality of sub-networks. The plurality of sub-networks include an underlying processing network output of the underlying processing network and perform operation processing on the output of the underlying processing network to output a label, and labels output by different output networks are different.

Abstract: A question-answer processing method, apparatus, electronic device, and computer-readable storage medium are provided. A question-answer processing method includes the following steps: acquiring an input question and a candidate question-answer pair, where the candidate question-answer pair comprises a candidate question and a candidate answer matching the candidate question; inputting the input question into a first model to obtain an input question vector; calculating a similarity between the input question vector and a candidate question vector, and calculating a relevance between the input question vector and a candidate answer vector; and determining a matching degree of the candidate answer and the input question according to the similarity and the relevance.

Abstract: The present disclosure relates to a method for training a natural language processing model, including: obtaining a sample text of natural language; determining one or more triples in the sample text, wherein each of the triples comprises two entities in the sample text and a relation between the two entities; processing the sample text based on the triples to obtain one or more knowledge fusion vectors; and training a natural language processing model by inputting the knowledge fusion vectors into the natural language processing model to obtain a target model.

Abstract: The present disclosure provides a method of processing medical data, which includes: acquiring first medical image data; inputting the first medical image data into a first feature extraction network to obtain a first image feature; and obtaining a first gene mutation information according to the first image feature. The first feature extraction network includes a first feature extraction module configured to: determine a first image query matrix and a first image key matrix according to the first medical image data; determine a first image weight matrix according to the first image query matrix and the first image key matrix, where the first image weight matrix represents a correlation information between each two first medical images in the first medical image data; and determine the first image feature according to the first image weight matrix and the first medical image data.

Abstract: The present disclosure provides a method and apparatus for predicting an RNA-protein interaction, a medium and an electronic device, relating to the field of artificial intelligence technologies. The method includes: acquiring a candidate RNA sequence and a candidate protein sequence; encoding the candidate RNA sequence to obtain an RNA vector sequence; encoding the candidate protein sequence to obtain a protein vector sequence; constructing a matching feature matrix according to the RNA vector sequence and the protein vector sequence; performing feature extraction on the matching feature matrix, and determining the interaction between the candidate RNA sequence and the candidate protein sequence according to the extracted matching feature.

Abstract: A method of generating a negative sample set for predicting macromolecule-macromolecule interaction is provided. The method includes receiving a positive sample set including pairs of macromolecules of a first type and macromolecules of a second type having macromolecule-macromolecule interaction; generating a first similarity map of the macromolecules of the first type; generating a second similarity map of the macromolecules of the second type; generating vectorized representations of nodes in the first similarity map and vectorized representations of nodes in the second similarity map; and generating the negative sample set using the vectorized representations of nodes in the first similarity map and the vectorized representations of nodes in the second similarity map.

Abstract: A method for training a vector model, including: obtaining more than one RNA sequence and more than one protein sequence; obtaining more than one first RNA vector by vectorizing the more than one RNA sequence; obtaining more than one first protein vector by vectorizing the more than one protein sequence; determining an interaction between the RNA sequence and the protein sequence according to the first RNA vector and the first protein vector; obtaining a similarity of more than one RNA-RNA pair by calculating a distance between any two RNA sequences; obtaining a similarity of more than one protein-protein pair by calculating a distance between any two protein sequences; training the vector model according to an interaction between the RNA sequence and the protein sequence, the similarity of the RNA-RNA pair and the similarity of the protein-protein pair.

Abstract: An RNA location prediction method and apparatus, and a storage medium. The method comprises: acquiring sequence feature information and structure feature information of LncRNA to be located (101); performing calculation on the sequence feature information and/or the structure feature information on the basis of an attention mechanism, to obtain an attention value of the sequence feature information and/or the structure feature information (102); and inputting the attention value into a classification prediction model to obtain a location prediction result of the LncRNA to be located (103).

Abstract: A linewidth measurement method and apparatus, a computing and processing device, a computer program and a computer readable medium are provided for measuring a width of a line.

Abstract: An RNA-protein interaction prediction method and device, a medium and an electronic device are provided. The method includes: obtaining an RNA-protein pair to be predicted; performing feature extraction on the RNA-protein pair to obtain sequence features of the RNA-protein pair; vectorizing the RNA-protein pair to obtain an RNA sequence representation vector and a protein sequence representation vector in the RNA-protein pair; based on the sequence features of the RNA-protein pair, the RNA sequence representation vector and the protein sequence representation vector in the RNA-protein pair, obtaining at least one predicted interaction value of the RNA-protein pair using at least one interaction prediction model; and determining interaction between the RNA and the protein according to the at least one predicted interaction value.

Abstract: The present disclosure provides an intelligent triage method and device, a storage medium and an electronic device, and is related to artificial intelligence technology. The method includes: obtaining disease information of a target outpatient, and obtaining a vector of the target outpatient according to the disease information; vectorizing each doctor in a doctor knowledge graph to obtain a plurality of doctor vectors; and calculating matching degrees between the vector of the target outpatient and the plurality of doctor vectors using a matching degree calculation model, and recommending a doctor for the target outpatient according to magnitudes of the matching degrees.

Abstract: Model training method, signal recognition method, apparatus, computing and processing device, computer program, and computer-readable medium.

Abstract: The present disclosure provides a method and device for disease risk prediction, a computer readable storage medium and an electronic device, which relates to the field of data processing. The method includes: obtaining risk feature data of a target user; and determining, by a disease risk prediction model based on the risk feature data, a disease risk value of the target user and a reliability score of the disease risk value.

Abstract: A knowledge graph updating method, an apparatus, an electronic device, a storage medium and a program thereof, relates to the field of computer technology. The method includes: receiving an updating request of a technical knowledge graph, the updating request includes: a technical-event node of the technical knowledge graph; extracting historical event information corresponding to the technical-event node from the technical knowledge graph; and updating event information of the technical-event node after a current time by using a target-technical-event prediction model; wherein, the target-technical-event prediction model is obtained by training a technical-event prediction model with the historical event information.