Gradient Boosting State Detection for Low-Latency Genetic Screening
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing genetic screening methods for diseases like Type 1 diabetes suffer from high latency due to complex machine learning models, require extensive computational resources, and are inaccurate due to additive models ignoring interactive effects between SNPs, leading to increased costs and delays in diagnosis.
Innovation Solution
A low-latency genetic screening system using gradient boosting models that account for interactive effects between SNPs, allowing for rapid prediction of genetic conditions even with incomplete data, and can be deployed on edge devices, reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning models are used for genetic screening, then prediction accuracy may improve, but execution time increases causing high latency
Solution Approach 1:
The patent segments the machine learning model into a simplified gradient boosting structure with decision trees, breaking down the complex prediction task into manageable components that can be executed rapidly. Each decision tree handles specific SNP interactions, allowing the system to achieve accurate predictions without the computational burden of fully complex models.
Solution Approach 2:
The patent changes the model parameters by using gradient boosting algorithms that optimize for both accuracy and speed. The model parameters are tuned to capture interactive effects between SNPs while maintaining efficient computation, transforming the traditional accuracy-speed tradeoff into a balanced performance profile suitable for clinical deployment.
2Device complexity
If additive models are used for genetic screening, then computational requirements are reduced, but prediction accuracy deteriorates due to ignoring interactive effects between SNPs
Solution Approach 1:
The patent extracts and specifically models the interactive effects between SNPs using gradient boosting decision trees, separating these complex interactions from the baseline additive model. This allows the system to capture non-linear relationships and SNP-SNP interactions that additive models miss, while keeping the overall computational structure manageable through targeted interaction modeling rather than full combinatorial analysis.
Solution Approach 2:
The patent creates a composite modeling approach that combines additive main effects with interactive effects modeled by gradient boosting decision trees. This composite structure integrates the simplicity of additive models with the power of interaction modeling, achieving high prediction accuracy by combining both approaches in a unified framework that handles both independent and joint SNP effects.
3Measurement precision
If comprehensive genetic screening is performed, then diagnostic accuracy improves, but costs and processing time increase
Solution Approach 1:
The patent applies partial action by focusing computational resources on modeling the most significant SNP interactions and effects rather than exhaustively analyzing all possible combinations. The gradient boosting algorithm efficiently identifies and processes only the relevant interactive effects, providing accurate diagnostics without the computational expense of comprehensive brute-force analysis of every genetic combination.
Data Source
AI summary
A system for low-latency state detection using gradient boosting. The system determines correlations between characteristics at certain locations and a target state. Using one or more locations determined to have a causal relation with the state, the system trains a gradient boosting-based model configured to accept, for input, a variant count for each of the one or more determined locations and output a confidence score indicating whether the first individual has the state. The system generates clusters for individuals based on impact features indicating an impact of the variant count on the confidence score. The clusters are associated with a manifestation of the state. The system can execute the machine learning model against a candidate individual to determine if they have the state, and in response to the state exceeding a threshold, determine the impact features to associate the individual with a cluster and determine likely manifestations of the state.