Missense mutation function classification method and system based on multiple omics characteristics

By constructing a heterogeneous graph and using node-level and semantic-level attention learning to calculate weights, and dynamically adjusting meta-paths and node importance, the problem of ignoring biological associations in existing missense mutation functional classification methods is solved, achieving higher classification accuracy and adaptability.

CN120708705APending Publication Date: 2025-09-26CENT SOUTH UNIV
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Patent Information

Application Number
CN202510801723.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-16
Publication Date
2025-09-26

AI Technical Summary

Technical Problem

Existing functional classification methods for missense mutations ignore the biological associations between different omics features, resulting in the model being unable to effectively capture the impact pathway of mutations on protein function and insufficient classification accuracy.

Method used

A heterogeneous graph was constructed, and the weights between multi-omics features were calculated through node-level and semantic-level attention learning. The importance of each meta-path and node was dynamically adjusted, and the missense mutation function was classified using a multi-layer perceptron.

Benefits of technology

Explicitly modeling the relationship between mutations and other biological entities improves the accuracy and interpretability of missense mutation functional classification and enhances the adaptability of the model to different biological contexts.

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Abstract

The invention relates to the technical field of biological gene mutation prediction, in particular to a multi-omics feature-based missense mutation function classification method and system. The classification method comprises the following steps: S1, collecting a multi-omics comprehensive feature set related to missense mutation; s2, constructing a heterogeneous graph; s3, learning a meta-path-based weight of each node in the heterogeneous graph; s4, calculating weights of different meta-paths by using attention of a semantic level; s5, performing weighted summation on the node weight and the meta-path weight to obtain final embedding representation of each node; s6, training to obtain a multi-omics feature-based missense mutation function classification model; and S7, outputting a prediction score based on the multi-omics feature missense mutation function classification model, and dividing missense mutation according to the prediction score. According to the method, the heterogeneous graph is constructed by utilizing multiple omics characteristics, and the relationship between biological entities is explicitly modeled, so that GOF and LOF mutations are distinguished more accurately.
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