一种基因-微生物-表型三元关系挖掘方法
By constructing gene-level GWAS association signals and employing a gradient boosting decision tree model, the problem of integrating gene-microbe-phenotype ternary relationships in existing technologies is solved, achieving efficient ternary relationship mining and enhanced result interpretability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INSTITUTE OF ANIMAL SCIENCES OF CHINESE ACADEMY OF AGRICULTURAL SCIENCES
- Filing Date
- 2026-06-22
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies struggle to integrate genomic, microbiome, and phenotypic data within the same framework, fail to effectively capture the gene-microbe-phenotype ternary relationship, suffer from low computational efficiency, and lack a systematic quantitative framework.
By constructing gene-level GWAS association signals, correcting SNP quantity bias, using a gradient boosting decision tree model for joint modeling, calculating multi-level correlation matrices and evaluating the comprehensive score of ternary relationships, and utilizing multi-omics data for ternary relationship mining.
It achieves a systematic integration of the gene-microbe-phenotype ternary relationship, eliminates SNP quantity bias, captures nonlinear interaction effects, provides multi-mode computation, and enhances the biological interpretability of the results.
Smart Images
Figure CN122417152A_ABST