Polygenic Risk Score ML Framework Using Bayesian Sampling
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Solution Overview
Problem
Existing health-related predictive data analysis systems face challenges in efficiently and accurately generating polygenic risk scores due to the computational complexity of selecting optimal genetic variant sets for predicting target phenotypes, as they often require brute-force traversal of potential parameter spaces and fail to integrate interactions between genetic variants.
Innovation Solution
The use of a comparatively-refined polygenic risk score generation machine learning framework that employs holistic Bayesian sampling routines to select an optimal genetic variant refinement model based on Bayesian evidence numerical estimates, allowing for efficient estimation of genetic variant weights and integration of interactions between genetic variants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute-force traversal of parameter spaces is used to select optimal genetic variant sets, then measurement precision of polygenic risk scores is improved, but device complexity and computational resources required increase significantly
Solution Approach 1:
The patent transforms the discrete combinatorial problem of selecting genetic variant sets into a continuous optimization problem by defining a likelihood function over variant effect sizes and using Bayesian inference to estimate parameters. This allows gradient-based optimization methods to be applied, dramatically reducing computational complexity while maintaining accuracy.
Solution Approach 2:
The patent replaces the mechanical brute-force traversal approach with a statistical inference framework using Bayesian sampling routines. Instead of exhaustively searching through all possible genetic variant combinations, the system uses probabilistic models to efficiently identify optimal variant sets based on observed phenotypic data.
2Productivity
If holistic Bayesian sampling routines are used to select optimal genetic variant refinement models, then productivity of model selection process is improved, but device complexity increases due to sophisticated sampling algorithms
Solution Approach 1:
The patent segments the model selection process into distinct stages: (1) defining candidate genetic variant refinement models with different variant sets, (2) performing Bayesian sampling to estimate model evidence for each candidate, and (3) selecting the model with highest evidence. This segmentation allows efficient parallel computation and reduces overall complexity.
Solution Approach 2:
The patent applies partial action by performing Bayesian sampling on a curated subset of plausible genetic variant refinement models rather than all possible models. This is achieved by pre-filtering candidates based on biological plausibility criteria and prior knowledge, reducing the computational burden while maintaining selection quality.
3Measurement precision
If multiple genetic variant refinement models are evaluated using Bayesian evidence, then measurement precision of model selection is improved, but loss of time and computational resources increases
Solution Approach 1:
The patent performs preliminary action by pre-computing likelihood contributions from individual genetic variants and pre-processing phenotypic data before the Bayesian sampling process. This preprocessing step caches intermediate results that are reused across multiple model evaluations, significantly reducing the time required for Bayesian evidence computation.
Solution Approach 2:
The patent uses copying by reusing the same Bayesian sampling routine and computational framework across all candidate genetic variant refinement models. Once the sampling infrastructure is established for one model, it can be efficiently replicated and applied to other models with different variant sets, avoiding redundant computation setup.
Data Source
AI summary
Various embodiments of the present invention describe techniques for generating a polygenic risk score generation machine learning framework that integrates an optimal genetic variant refinement model without requiring brute-force traversal of potential parameter spaces defined by various distinct genetic variant sets. In response, various embodiments of the present invention use holistic Bayesian sampling routines to efficiently generate Bayesian evidence numerical estimates for various genetic variant refinement models and select an optimal genetic variant refinement model accordingly. This enables enhancing the accuracy of polygenic risk score generation machine learning frameworks without resorting to computationally resource-intensive traversals of potential parameter spaces defined by various distinct genetic variant sets. In doing so, various embodiments of the present invention enhance the computational efficiency of generating a polygenic risk score generation machine learning framework that integrates an optimal genetic variant refinement model in contrast to computationally-inefficient techniques that require brute-force traversal of potential parameter spaces.