Genomic Sequence Selection Using Metadata-Aware ML Ensembles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI/ML approaches for antigen characterization in viral diagnostics and therapeutic/prophylactic interventions lack explainability, accuracy due to high mutation rates, and fail to optimally utilize genomic sequencing data for selecting sequences suitable for downstream applications.
Innovation Solution
A system and method that utilizes machine learning to analyze genomic sequences with associated metadata, leveraging a balanced bagging ensemble framework to select sequences that satisfy intent-specific criteria for therapeutic or prophylactic uses, incorporating host and environmental factors, and enabling real-time, on-demand suitability assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI/ML approaches are used for antigen characterization, then sensitivity of viral diagnostics is improved, but explainability of biological findings deteriorates
Solution Approach 1:
The patent introduces an intermediary layer between raw genomic data and diagnostic conclusions by incorporating metadata (sample collection information, host factors, environmental data) that bridges the gap between complex AI/ML predictions and interpretable biological insights. This metadata acts as a mediator that preserves explainability while maintaining the sensitivity benefits of AI/ML approaches.
2Measurement precision
If AI/ML approaches are used for antigen characterization, then prediction capability is improved, but accuracy due to high mutation rates deteriorates
Solution Approach 1:
The system dynamically adapts to high mutation rates by continuously updating its analysis framework. Instead of relying on static training data, the patent incorporates real-time genomic sequencing data and metadata that capture current pathogen variants, allowing the AI/ML models to maintain accuracy despite ongoing mutations. The system's ability to process and integrate new data dynamically ensures reliability in the face of evolutionary changes.
3Measurement precision
If genomic sequencing data is analyzed comprehensively, then characterization accuracy is improved, but computational complexity deteriorates
Solution Approach 1:
The patent segments the comprehensive genomic sequencing data into manageable components by organizing information into structured metadata categories (sample information, host factors, environmental data, sequence characteristics). This segmentation allows the complex data to be processed systematically through modular computational steps, reducing overall computational complexity while maintaining comprehensive characterization accuracy.
4Measurement precision
If extensive genomic sequence data is processed, then suitability assessment for therapeutic uses is improved, but processing time deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring genomic data into standardized metadata formats during data collection and initial sequencing stages. This preliminary organization includes pre-categorizing sequences by relevant characteristics and pre-filtering for potential therapeutic relevance, which significantly reduces the processing time required for subsequent suitability assessments while maintaining comprehensive evaluation accuracy.
Data Source
AI summary
Provided herein are systems and methods for selecting sequences that satisfy intent-specific criteria. In particular, provided herein are systems and methods which analyze sequences based on metadata and known, inferred, and experimentally determined attributes to select sequences for desired utilities (e.g., suitable for therapeutic/prophylactic interventions).