Deep Mutational Learning Framework for Genetic Variant Classification
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Solution Overview
Problem
Current methods for interpreting the phenotypic impacts of genotypic variants in genetic and genomic tests face challenges due to high numbers of uncharacterized variants, limited scalability, and reliance on computational predictors with low accuracy, which hinders the prediction of clinical consequences and leads to inconsistent classifications across laboratories.
Innovation Solution
The development of a multi-functional platform integrating high-throughput molecular measurements, single-cell manipulation, and statistical learning techniques to systematically assess the phenotypic impacts of variants across various biophysical processes and molecular functions, enabling robust and scalable classification of molecular variants through Deep Mutational Learning (DML) systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computational predictors are used for variant classification, then classification speed is improved, but accuracy deteriorates
Solution Approach 1:
The patent combines multiple computational predictors (SIFT, PolyPhen-2, CADD, REVEL) into an integrated ensemble system that aggregates their outputs. This merging approach leverages the strengths of individual predictors while compensating for their individual weaknesses, achieving both high speed (through parallel computational processing) and high accuracy (through ensemble consensus) in variant classification
Solution Approach 2:
The patent develops a universal classification framework that can handle multiple types of genetic variants (missense, nonsense, frameshift, splice site) across different genes and functional elements. The system uses a standardized scoring and ranking mechanism that works universally across diverse variant types, eliminating the need for separate classification pipelines for each variant category while maintaining high accuracy through consistent multi-predictor evaluation
2Reliability
If more variants are characterized, then diagnostic coverage is improved, but resource requirements worsen
Solution Approach 1:
The patent transforms the classification problem by changing the parameter space from individual variant evaluation to population-level statistical analysis. By converting raw predictor scores into standardized z-scores and ranking variants within gene-specific distributions, the system can efficiently prioritize the most likely pathogenic variants from large sets of uncharacterized variants, expanding diagnostic coverage while minimizing resource expenditure through focused follow-up on top-ranked candidates
Solution Approach 2:
The patent performs preliminary computational filtering and ranking of variants before experimental validation. By pre-processing large numbers of variants through multiple computational predictors and ranking them by predicted pathogenicity, the system identifies a small subset of high-priority variants for further characterization. This preliminary action dramatically reduces the number of variants requiring expensive experimental validation, thereby expanding overall diagnostic coverage while conserving resources
3Ease of operation
If traditional classification methods are used, then ease of operation is maintained, but consistency across laboratories worsens
Solution Approach 1:
The patent implements a feedback mechanism where classification results from multiple predictors are aggregated and used to adjust the prioritization of variants. The system continuously refines variant rankings based on consensus scoring, where variants that receive high scores across multiple independent predictors are elevated in priority. This feedback loop ensures that classification decisions are based on convergent evidence from multiple sources, dramatically improving inter-laboratory consistency while maintaining operational simplicity through automated scoring algorithms
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for determining phenotypic impacts of molecular variants identified within a biological sample. Embodiments include receiving molecular variants associated with functional elements within a model system. The embodiments then determine molecular scores associated with the model system. The embodiments then determine molecular signals and population signals associated with the molecular variants based on the molecular scores. The embodiments then determine functional scores for the molecular variants based on statistical learning. The embodiments then derive evidence scores of the molecular variants based on the functional scores. The embodiments then determine phenotypic impacts of the molecular variants based on the functional scores or evidence scores.