Genetic locus mutation prognostic risk assessment method, electronic device and storage medium
By acquiring and preprocessing genosite mutation data, using proportional hazards regression model and integrated classification model, the prognostic risk of genosite mutations is evaluated, and the problem of inaccurate evaluation in the existing technology is solved, and the evaluation accuracy and clinical reference value are improved.
WO2025065483A1 Publication Date: 2025-04-03BOE TECHNOLOGY GROUP CO LTD
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Patent Information
- Application Number
- PCT/CN2023/122493
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2023-09-28
- Publication Date
- 2025-04-03
AI Technical Summary
Technical Problem
The prior art cannot accurately evaluate the prognostic risks caused by genolo mutations, which will affect the formulation of clinical plans.
Method used
A prognostic risk assessment method for genolo-mutation was used to obtain the initial data set and control data set, pre-processing and proportional hazard regression model analysis, and combined with an integrated classification model, the prognostic risk type of genolos was determined.
Benefits of technology
It improves the accuracy of risk assessment of gene mutation prognosis risk, provides more comprehensive clinical reference information, and helps doctors formulate more effective clinical plans.
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Abstract
Provided in the embodiments of the present disclosure are a genetic locus mutation prognostic risk assessment method, an electronic device and a storage medium. The method comprises: acquiring an initial data set and a control data set, the initial data set being obtained on the basis of at least one genetic locus of a target gene being mutated, and the control data set being obtained on the basis of none of the gene loci of the target gene being mutated; preprocessing the initial data set to obtain a target gene mutation data set; using the proportional hazards model to determine a prognostic risk value of each gene locus in the target gene mutation data set; according to the prognostic risk values, selecting a target gene locus; and using an ensemble classification model to obtain a prognostic risk type result of the target gene locus.
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