Mutation Profile Genomic Risk Prediction Segmentation
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Solution Overview
Problem
Current methods face challenges in accurately determining genetic predisposition for conditions associated with genomic factors, such as cancer, obesity, and diabetes, due to the complexity of identifying reliable and accurate parameters for disease risk assessment.
Innovation Solution
A mutation profile is developed that represents the history of repeat regions in an individual's genome, characterized by error numbers and copy numbers, which can be used to build a classifier to predict condition risk propensity by comparing individual mutation profiles with reference profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to identify genomic factors associated with disease risk, then research progress is made, but identification of reliable and accurate parameters for disease risk determination remains challenging
Solution Approach 1:
The patent segments the complex task of disease risk assessment by dividing the genome into specific repeat regions and analyzing their mutation histories independently. Each repeat region's error number and copy number are calculated separately, then aggregated into a comprehensive mutation profile. This segmentation transforms the overwhelming complexity of whole-genome analysis into manageable, discrete units that can be processed and evaluated systematically.
Solution Approach 2:
The patent applies preliminary action by pre-calculating and storing the mutation profiles (error numbers and copy numbers) for repeat regions before disease risk assessment is needed. These pre-computed profiles serve as a foundation that can be quickly referenced and compared against disease-associated patterns, eliminating the need for complex real-time calculations during actual risk determination and improving both speed and accuracy.
2Reliability
If mutation profiles are used to predict condition risk propensity, then early screening capability is improved, but the complexity of analyzing genome evolution over time increases
Solution Approach 1:
The patent uses copying by creating a simplified representation (mutation profile) that copies essential information from the complex genome evolution history. Instead of storing and analyzing the entire evolutionary trajectory, the system copies only the critical metrics (error numbers and copy numbers) into a compact profile format. This copied profile can be easily stored, transmitted, and compared, maintaining the reliability of early screening while dramatically reducing analytical complexity.
Solution Approach 2:
The patent applies parameter changes by transforming the complex, continuous data of genome evolution into discrete, quantifiable parameters (error number m and copy number d). This parameter transformation converts the intricate problem of tracking genomic changes over time into a standardized numerical format that can be processed using established statistical and machine learning methods, improving reliability while simplifying the analysis.
Data Source
AI summary
A mutation profile can be determined for an individual's DNA sequence or sequence segment that provides information about the evolutionary history of the DNA. This mutation profile can then be used with a machine learning classifier trained on other people's mutation profiles to determine probabilities that the individual has certain phenotypes. An example is cancer, where the probabilities of different types of cancer can be provided in a disease risk propensity.