36 results about "Disease/disorder" patented technology

To provide a AI inquiry device, a AI inquiry method, and a program capable of reducing a burden on a patient during AI inquiry.SOLUTION: The AI inquiry device includes an image inputting part, a disease candidate estimating part, and a disease estimating part. The image input unit receives an input of a face image of a patient. The disease candidate estimation unit extracts symptoms of the patient from the face image of the patient to estimate disease candidates of the patient based on the symptoms of the patient by using the image recognizing AI model. The disease estimation unit uses the interactive AI model to output a question to the subject, receive an input of a response of the subject to the question, and estimate a disease of the subject from candidates for the disease of the subject based on the response of the subject.SELECTED DRAWING: Figure 2

The application discloses a drug repositioning method and system based on heterogeneous knowledge and structure fusion, and belongs to the technical field of medical care informatics. The method comprises the following steps: constructing a biomedical field knowledge heterogeneous graph; based on the biomedical field knowledge heterogeneous graph, generating disease knowledge embedding and drug knowledge embedding corresponding to a target drug-disease pair; by constructing a drug-drug similarity network, a disease-disease similarity network and a drug-disease association network, generating disease structure embedding and drug structure embedding corresponding to the target drug-disease pair; and based on the disease knowledge embedding, the drug knowledge embedding, the disease structure embedding and the drug structure embedding, obtaining a drug repositioning result. The application accurately captures complex biological network characteristics and finely models complex entity information through an innovative drug repositioning model, so that accurate drug repositioning is realized.

The application relates to the technical field of bioinformatics, in particular to a drug repositioning method based on reinforced symmetric metric learning and a graph convolution network, which comprises the following steps: a drug-disease heterogeneous network is constructed by integrating the correlation of drugs and diseases and biomedical information. The heterogeneous network comprises a drug-drug similarity network, a disease-disease similarity network and a drug-disease correlation network. A graph convolution network is applied to learn the node features of drugs and diseases, and potential drug-disease correlations are predicted to supplement the missing drug-disease correlation information. A reinforced symmetric metric learning method with adaptive margins is used to learn the potential vector representation of drugs and diseases, and the symmetric learning of drug-centered and disease-centered is considered. Based on the potential vector representation learned in the unified metric vector space, new drug-disease correlations are identified through a metric function. The application is simple and effective, and has good performance in drug repositioning prediction.

The invention provides antibodies, antibody-drug conjugates, bispecific T cell engagers (BiTEs), and chimeric antigen receptors (CARs) that target transmembrane activator and calcium modulator and cyclophilin ligand interactor (TACI). Such antibodies, antibody-drug conjugates, BiTEs, and CARs can be used, e.g., in methods for treating a cancer (e.g., multiple myeloma), an autoimmune disorder (e.g., characterized by a high titer of antibodies contributing to the disorder), or a plasma cell disease disorder (e.g., plasma cell dyscrasias) in a subject in need thereof.