A gene mutation pathogenicity automatic rating method and system based on multi-agent game and thought chain reasoning

By employing multi-agent game theory and thought chain reasoning, the efficiency and accuracy issues in interpreting the pathogenicity of gene mutations are addressed. This approach enables a deep understanding and logical adversarial analysis of unstructured evidence, generates interpretable rating reports, and improves the accuracy and consistency of automated rating of the pathogenicity of gene mutations.

CN121884938BActive Publication Date: 2026-07-03BEIJING NUTSHELL BIOTECHNOLOGY CO LTD
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Patent Information

Application Number
CN202610344027.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-20
Publication Date
2026-07-03
Estimated Expiration
2046-03-20

AI Technical Summary

Technical Problem

Existing technologies suffer from inefficiency, inaccuracy, and poor consistency in interpreting the pathogenicity of gene variations. In particular, they struggle to effectively understand and eliminate model illusions when dealing with unstructured literature evidence, leading to inconsistent variation rating results.

Method used

We employ a multi-agent game theory and thought chain reasoning approach, using proposer, refutator, and decision-maker agents for multi-round iterative evaluation. We combine large language models to parse unstructured data and introduce phenotypic and structural biology expert agents. We use weighted voting mechanisms and Bayesian weighting algorithms for comprehensive evaluation and monitor confidence levels in real time to dynamically truncate the iteration process.

Benefits of technology

It significantly improves the accuracy and consistency of pathogenicity rating of gene variants, reduces the model illusion rate, enhances the ability to understand unstructured evidence, saves computing resources and time costs, and generates interpretable rating reports.

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Abstract

The present application relates to the technical field of the cross of bioinformatics, medical genetics and artificial intelligence, and discloses a gene mutation pathogenicity automatic rating method and system based on multi-agent game and thought chain reasoning, which comprises the following steps: searching data related to a target mutation site; based on the related data; performing multi-round iterative gene mutation pathogenicity rating by a proposer agent, a refuter agent and a decision maker agent; monitoring the rating conclusion output by the decision maker agent and the confidence score in real time, and when a preset dynamic truncation condition is met, terminating the multi-agent game step and outputting the final gene mutation pathogenicity rating conclusion. The present application can realize accurate and transparent rating of gene mutation pathogenicity by eliminating model hallucinations through adversarial games and analyzing unstructured documents by using thought chains.
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