Qnet Genomic Mutation Prediction for Viral Jump Risk
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Solution Overview
Problem
Current surveillance paradigms are insufficient for accurately predicting the risk of viral strain emergence and inter-species jumps, as they fail to quantify the likelihood of mutations occurring in the wild and do not account for the constraints arising from the need to conserve function, leading to subjective and inaccurate assessments of viral jump-risk and dominant strains.
Innovation Solution
A method involving the calculation of a Qnet for genomic sequences using conditional inference trees to model evolutionary constraints, allowing for the computation of a 'q-distance' that quantifies the likelihood of viral sequence mutations, thereby predicting dominant strains and inter-species jump risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current surveillance paradigms are used to assess viral jump risk, then the assessment process is simple and quick, but the accuracy and reliability of the risk prediction is low
Solution Approach 1:
The patent introduces q-distance as an intermediary metric that bridges the gap between simple sequence comparison and complex evolutionary modeling. By using conditional distributions derived from aligned sequences as an intermediate representation, the system can quantify mutation likelihood without requiring full evolutionary simulations, thus improving accuracy while maintaining computational feasibility
Solution Approach 2:
The patent transforms the prediction approach by changing the parameter being measured from simple sequence similarity to q-distance, which incorporates conditional probabilities of mutations. This parameter transformation allows the system to capture evolutionary constraints and functional conservation without requiring complex mechanistic models of viral evolution
2Reliability
If the number of mutations between sequences is used to measure similarity, then the calculation is straightforward, but the assessment does not account for mutation likelihood and functional constraints
Solution Approach 1:
The patent replaces the mechanical counting of mutations with a probabilistic model based on conditional distributions. Instead of simply counting sequence differences, the system uses Qnet to calculate the probability of each mutation occurring, accounting for functional constraints and evolutionary likelihood, thus improving reliability without excessive complexity
3Measurement precision
If computational methods are developed to calculate precise mutation likelihood, then the accuracy of risk assessment improves, but the computational complexity and resources required increase
Solution Approach 1:
The patent applies partial action by calculating q-distance only for the specific indices and sequences that are most relevant to jump risk assessment. Rather than performing exhaustive calculations on all possible sequence combinations, the method focuses computational resources on the most critical comparisons, achieving high precision with reduced computational overhead
Solution Approach 2:
The patent performs preliminary calculations by pre-computing conditional distributions from aligned sequences and storing them in Qnet. These pre-computed distributions are then reused for multiple risk assessments, avoiding redundant calculations and reducing the computational resources needed for each new prediction
Data Source
AI summary
A method includes receiving a first plurality of aligned genomic sequences of a virus from a database. The aligned genomic sequences have a first common background. The method includes calculating a Qnet for each genomic sequence of the first plurality of aligned genomic sequences. The Qnet for each sequence is calculated by calculating a conditional inference tree for each index of the aligned genomic sequences using other indices in the aligned genomic sequences as predictive features, and calculating predictors for indices that were used as predictive features when calculating the conditional inference tree for each index.