Machine Learning Gene Panel Selection for User-Specific Predictions

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Solution Overview

Problem

Standard genetic testing panels do not account for user-specific relevance or effectiveness of protocols in preventing or modifying conditions, often yielding incomplete or misrepresentative results due to insufficient user data.

Innovation Solution

The use of machine learning or artificial intelligence models to process user-specific data sets, predicting the effectiveness of protocols in modifying expected results associated with conditions, and selecting a tailored panel of genes for testing based on these predictions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a standard panel of genes is used for testing all users, then the testing process is simple and standardized, but the results are not informative or relevant to individual users because user-specific factors are not considered

Engineering Contradiction:

Improveuser-specific relevance of testing resultsVSAvoidcomplexity of panel selection process

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by collecting and analyzing user-specific data (demographics, medical history, lifestyle factors) before determining the gene panel composition. This preliminary analysis enables customization of the panel for each user, ensuring relevance while managing complexity through automated data processing and machine learning algorithms that evaluate multiple factors to recommend appropriate panel compositions.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If user-specific data is collected and analyzed to customize gene panels, then the relevance and informativeness of results improves, but the data requirements become more complex and data quality issues arise

Engineering Contradiction:

Improveprecision of protocol effectiveness predictionVSAvoidincompleteness of user data

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The system implements feedback mechanisms where machine learning models are trained on user data and outcomes, continuously improving prediction accuracy for protocol effectiveness. The feedback loop allows the system to learn from accumulated data, refine its predictions, and adapt to individual user patterns, thereby improving measurement precision while managing data quality challenges through iterative model training and validation.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If machine learning models are used to predict protocol effectiveness for individual users, then the accuracy of predictions improves, but the computational complexity and processing requirements increase

Engineering Contradiction:

Improveaccuracy of protocol effectiveness predictionVSAvoidcomplexity of machine learning model processing

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the gene panel into multiple components or modules that can be independently evaluated and combined. This segmentation allows the machine learning model to process complex predictions by breaking down the overall panel effectiveness into contributions from individual genes or gene groups, thereby improving prediction accuracy while managing computational complexity through modular processing and interpretation of model outputs.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20230316092A1Systems and methods for enhanced user specific predictions using machine learning techniques

Publication Date: 2023.10.05 COLOR HEALTH INC

AI summary

Data sets can be processed using machine learning or artificial intelligence models to generate outputs predictive of a degree to which performing a protocol can positively modify an expected result associated with a condition. Generating the output may include accessing a user data set, inputting the user data set into a trained machine learning model to generate an output, and selecting an incomplete subset of a set of genes based on the output.