Machine Learning Variant Effect Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting the effects of genetic variants are limited as they only consider perturbations at one site relative to a reference sequence, failing to account for interactions between multiple genetic variants, which is particularly problematic in complex phenotypes like cancer where multiple mutations occur simultaneously.
Innovation Solution
A machine learning-based approach that assesses the combined impact of multiple genetic variants by using a trained model to generate effect scores for secondary genetic variants, allowing for the prediction of compensatory effects and their magnitudes, and integrating these into various applications such as synthetic biology, personalized medicine, and genetic engineering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional variant effect prediction tools (PolyPhen, SIFT, Provean, GERP) are used, then prediction of single-site genetic variant effects is achieved, but the ability to predict interactions between multiple genetic variants is lost
Solution Approach 1:
The patent combines multiple single-variant prediction tools (PolyPhen, SIFT, Provean, GERP) into an integrated system that simultaneously evaluates multiple genetic variants and their interactions, enabling both single-variant precision and multi-variant interaction prediction
Solution Approach 2:
The integrated system performs multiple functions: predicting single-variant effects, predicting multi-variant interactions, and providing comprehensive variant effect assessments, making it universally applicable to diverse genetic analysis needs
2Measurement precision
If machine learning-based integrated assessment of multiple genetic variants is implemented, then prediction accuracy of multi-variant interactions is improved, but computational complexity increases
Solution Approach 1:
The machine learning model processes genetic variants in structured segments (individual variants and their combinations), breaking down the complex multi-variant assessment into manageable computational units that can be evaluated systematically
Solution Approach 2:
The patent introduces computational intermediaries (feature extraction layers, embedding representations) that transform raw genetic sequence data into simplified intermediate representations, reducing the complexity burden on subsequent prediction layers
3Measurement precision
If comprehensive multi-variant genetic sequence analysis is performed, then prediction accuracy of phenotypic consequences is improved, but analysis time increases
Solution Approach 1:
The system performs preliminary filtering and prioritization of genetic variants based on individual variant scores before conducting comprehensive multi-variant interaction analysis, reducing the computational scope and time required for full assessment
Solution Approach 2:
The patent implements a tiered analysis approach where essential multi-variant interactions are evaluated first (partial action), providing sufficient accuracy for most applications without performing exhaustive analysis of all possible variant combinations (excessive action)
Data Source
AI summary
Provided herein are machine learning-based methods for assessing the combined impact of multiple genetic variants, as well as the uses of such methods for various applications, such as in synthetic biology, personalized medicine, agricultural breeding, and genetic engineering. Also provided herein are exemplar computer-readable storage media and electronic devices for performing such methods.