Gene Mutation Assessment Device Regional Scoring
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Solution Overview
Problem
Current gene mutation analysis methods often miss associations between gene mutations and traits due to detection errors, leading to false negatives when focusing on single locus mutations.
Innovation Solution
A gene mutation assessment system that communicates with a database to acquire mutation information, assigns scores based on trait association, re-scores mutations with low initial scores by considering associated regions, and determines assessment scores to identify potentially associated mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If gene mutation analysis focuses on single locus mutations, then the analysis process is simple, but the detection accuracy decreases due to false negatives from detection errors and measurement errors
Solution Approach 1:
The patent combines multiple single locus mutation analyses into a regional mutation analysis. By merging the assessment of multiple mutations within a genomic region and applying a scoring system that aggregates evidence, the system achieves more accurate detection of trait-associated mutations while maintaining operational simplicity through automated processing.
Solution Approach 2:
The patent transitions from one-dimensional single locus analysis to multi-dimensional regional analysis. By considering mutations across multiple loci simultaneously and evaluating their collective scoring patterns, the system detects associations that would be missed in single locus analysis, thereby improving measurement precision without significantly increasing operational complexity.
2Quantity of substance
If exhaustive analysis of gene mutations is performed using next-generation sequencer or microarray, then the quantity of mutation candidates increases, but the difficulty of determining which mutation is associated with which trait increases
Solution Approach 1:
The patent applies parameter changes by introducing a scoring system that transforms the complex data from exhaustive mutation analysis into ranked results. By assigning scores based on multiple parameters (frequency in sample group, regional mutation patterns, database comparisons), the system automatically prioritizes likely candidates, reducing the difficulty of determining trait-mutation associations.
Solution Approach 2:
The patent applies local quality by focusing analysis on specific genomic regions with multiple mutations rather than treating all mutations uniformly. By identifying regions where multiple mutations occur and applying regional scoring rules, the system efficiently identifies trait-associated mutations among large numbers of candidates without requiring exhaustive evaluation of each individual mutation.
3Measurement precision
If re-scoring is performed for mutations with low initial scores by considering associated regions, then the accuracy of mutation identification improves, but the device complexity increases
Solution Approach 1:
The patent applies preliminary action by first performing initial scoring of all mutations, then selectively re-scoring only those with low initial scores. This two-stage approach improves mutation identification accuracy by focusing computational resources on borderline cases while avoiding unnecessary complexity in clearly positive or negative cases, thereby balancing precision with system complexity.
Data Source
AI summary
The present invention provides a new gene mutation assessment system which makes it possible, even when a gene mutation has considered to be apparently not associated with a trait from mutation information at a single position, to pick the gene mutation as a gene mutation candidate showing an association with the trait. The gene mutation assessment device (10) of the present invention includes a communication unit (19) that is capable of communicating a database DB, an assessment target mutation information acquisition unit (11) that acquires mutation information of a common gene mutation in a sample group showing a common trait as mutation information of an assessment target mutation, a score assignment unit (12) that assigns a first score showing an association with a trait of the DB information to the assessment target mutation based on the DB information, a score determination unit (13) that compares the first score with an association threshold and determines the assessment target mutation as a re-scoring target when the first score is less than the association threshold, a region mutation information acquisition unit (14) that acquires, as region mutation information, a gene mutation in an associated region with respect to a re-scoring target assessment target mutation based on the DB information, a score re-assignment unit (15) that assigns a second score weighted to the first score to the re-scoring target assessment target mutation based on the region mutation information, and an assessment score determination unit (16) that determines the second score as an assessment score of the re-scoring target assessment target mutation.