Method for classifying multi-granularity breast cancer genes based on double self-adaptive neighborhood radius
US11837329B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 22, 2022 |
| Grant date | Dec 5, 2023 |
| Priority date | — |
| Expiry date | Feb 22, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H50/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for classifying multi-granularity breast cancer genes based on a double self-adaptive neighborhood radius includes large-scale gene locus data are read and normalized, and a data analysis is performed on the large-scale gene loci. An optimum value K is selected by adopting a combination of contour coefficients and a PCA dimensionality reduction visualization, and a model of information granulation is adjusted. A heuristic reduction algorithm is used to implement a multi-granularity attribute reduction of a self-adaptive neighborhood radius based on a cluster center distance and a multi-granularity attribute reduction of a neighborhood radius based on an attribute inclusion degree, and big data for breast cancer genes are classified and predicted by adopting a machine learning classification algorithm based on a SVM support vector machine.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.