38 results about "Subtype classification" patented technology

The invention belongs to the technical field of medicine, and specifically relates to a method and system for recommending antipsychotic drugs based on subtype classification of mental disorders. The present invention uses artificial intelligence and machine learning technology to classify given mental disorders through mining and analysis of mental disorder diagnosis and treatment scale data; the present invention specifically includes: bi-clustering subtype classification based on mental disorder scale evaluation data ; Use machine learning algorithms and scale data before treatment to establish a post-treatment evaluation prediction model; Drug analysis recommendation for schizophrenia: For each schizophrenia patient and initial disease judgment, use the scale scoring data and patient information to divide its subtype; according to the patient's scale index component data and the patient's basic information, the evaluation prediction model is executed for each candidate drug; the drug that can enter the superior subtype and / or the total score of the scale is reduced the most after treatment is used as Drugs are recommended. The invention can realize accurate and objective auxiliary diagnosis and treatment for diseases and improve curative effect.

Described herein are gene signatures providing prognostic, diagnostic, treatment and molecular subtype classifications of ovarian cancers through generation of ovarian cancer disease signatures (OCDSs) that account for molecular heterogeneity present in gynecological cancers. An ovarian cancer fixed signature (OCFS) is described which relates to the core programming of disease development, in addition to an ovarian cancer stem cell (OCSC) signature. Development various disease signature, suggests personalized treatment strategies focused on molecular subtypes of gynecological cancers, such as triage tests for patients.

The invention relates to a precise intelligent diagnosis and treatment big data system which comprises a centralized data management module, a data preprocessing module, a marker extraction module, asubtype classification module and a medicine reaction prediction module, wherein the centralized data management module is used for centrally managing clinical electronic medical history data and omics data of multiple medical institutes; the data preprocessing module is used for preprocessing the centrally managed data and used for establishing a relationship dependency net on the basis of biomedicine characteristics; the marker extraction module is used for extracting characteristics genes of patients to obtain marker sets on the basis of preprocessed data; the subtype classification moduleis used for classifying subtypes of the patients and used for confirming corresponding groups of the patients; the medicine reaction prediction module is used for establishing medicine reaction prediction models and used for predicting reactions of the patients upon different medicines according to the medicine reaction prediction models. Compared with the prior art, the system is capable of achieving effective management on medical data and medicine reaction prediction, and intelligentization can be achieved.

The invention discloses a tumor immune subtype classification method and system, the method comprising: acquiring a sample data set including RNA-seq sequencing data and gene expression profile data of multiple tumor tissues; Corresponding microbial abundance data, immune cell ratio data, and immune-related gene expression data in the corresponding tumor tissue; use microbial abundance data, immune cell ratio data, and immune-related gene expression data as classification features to construct training sample data set, and use the SMOTE algorithm to expand the minority class, train the random forest model, and improve the random forest model in the form of weighting: increase the weight of the minority class, make the decision tree classifier focus on the minority class, and improve the minority class. class classification accuracy. The invention can improve the accuracy of tumor immune subtype classification and prediction, and provide a new target for tumor immunotherapy.

The present invention provides a group of long-chain noncoding RNA (lncRNA) markers for tumor immunophenotyping, including 49 lncRNAs in total. The present invention also provides a method for tumor immunophenotyping based on the above-mentioned lncRNA markers, which divides 49 lncRNAs into groups, and combines the expression of cytotoxic T lymphocytes (CTLs) to further classify patients into hierarchical clusters with immune activation Immune-Active Class, Immune-Exclusion Class, Immune-Dysfunctional Class and Immune-Desert Class. The tumor immune subtype classification method of the present invention has a strong correlation with predicting the overall survival rate and responsiveness of anti-tumor immunotherapy, and has the characteristics of high objectivity, accuracy and repeatability. The establishment of this classification method provides predictive indicators for the efficacy and prognosis of anti-immunotherapy in patients with malignant tumors.