Viral Mutation Prediction Using RNA Editing Analysis
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Solution Overview
Problem
Current diagnostic and therapeutic agents for RNA viruses, such as the novel coronavirus, become ineffective due to viral mutations, as existing methods cannot predict mutations in advance, requiring time-consuming reformation of antibody and antigen tests and therapeutic agents after mutations occur.
Innovation Solution
A viral mutation prediction device and method that acquires gene sequence data, extracts specific base mutations, separates amino acid changes, and uses machine learning to predict future mutations by focusing on RNA editing enzymes and characteristic sequences, enabling early preparation of diagnostic and therapeutic agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If viral mutation prediction is not conducted, then diagnostic and therapeutic agents remain effective for longer periods, but when mutations occur, significant time is lost in identifying mutation sites and creating new agents
Solution Approach 1:
The patent applies preliminary action by predicting viral mutations before they actually occur. The machine learning model analyzes genomic sequences and RNA editing enzyme activity patterns to forecast potential mutation sites, allowing researchers to prepare updated diagnostic and therapeutic agents in advance rather than waiting for mutations to manifest and then reactively developing new agents.
2Measurement precision
If machine learning prediction of mutations is implemented, then prediction accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies the extraction principle by isolating and focusing on specific, high-value features from the complex genomic data. Rather than analyzing all possible sequence variations, the system extracts and prioritizes features related to RNA editing enzyme recognition sites and known mutation hotspots, reducing computational complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent uses an intermediary approach by introducing a machine learning model that acts as a mediator between raw genomic sequence data and mutation predictions. This intermediary layer processes and interprets complex biological data patterns, translating them into actionable predictions without requiring direct complex analysis of all genomic features.
3Reliability
If comprehensive genomic sequence analysis is performed to identify all potential mutations, then prediction completeness improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific high-risk regions of the viral genome rather than uniformly analyzing all sequences. The system identifies and prioritizes analysis of regions with known RNA editing enzyme recognition motifs and historical mutation hotspots, achieving comprehensive prediction of clinically relevant mutations while improving processing efficiency.
Data Source
AI summary
A viral mutation prediction device has an acquisition unit which acquires gene sequence data of a genome of a virus, an extraction unit which extracts C (cytosine) or G (guanine) from the acquired gene sequence data of the genome and extracts contexts in which a mutation from C or G to U (uracil) occurs or has occurred, a separation unit which checks whether there is an amino acid mutation when C or G has changed to U and which separates sequences with the amino acid mutation as nonsynonymous substitutions and separates sequences without the amino acid mutation as synonymous substitutions, a learning unit which learns using the sequence data of the synonymous substitutions for learning data and a prediction unit which predicts a mutation of the virus using the learned results.