AI Multiomics Therapy Selection Through Genome Variant Analysis
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Solution Overview
Problem
Current drug discovery processes for rare diseases are inefficient, costly, and prone to misdiagnosis, with conventional models being treatment-specific, expensive, and suffering from accuracy and repeatability issues, while deep learning models face data bias and high training costs.
Innovation Solution
An AI-based computing system that analyzes patient multiomics data to detect genome variants, determine functional impacts, and recommend therapies through a series of modules including variant detection, functional rescue, and drug discovery, utilizing AI models for accurate and efficient therapy determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional first principle-based models are used for drug discovery, then treatment-specific accuracy is improved, but computational cost and complexity increase significantly
Solution Approach 1:
The patent segments the drug discovery process into multiple specialized AI models, each handling specific tasks (variant determination, coding determination, functional determination, therapy recommendation) rather than using a single complex model. This divides the computational complexity into manageable segments while maintaining high accuracy in each domain.
Solution Approach 2:
The system dynamically changes computational parameters by selecting different AI models and analysis depths based on the specific disease type, variant classification, and functional impact assessment needs. This allows the system to optimize computational resources while maintaining diagnostic accuracy.
2Adaptability or versatility
If deep learning artificial intelligence models are used for drug discovery, then uniform applicability is improved, but training cost and data bias issues worsen
Solution Approach 1:
The patent implements preliminary action by pre-training specialized AI models on specific datasets for different disease types and functional assessments before actual use. This allows the models to be ready for deployment without requiring extensive training during clinical application, reducing training time while maintaining uniform applicability across different diseases.
Solution Approach 2:
The system achieves universality through a modular architecture where multiple AI models work together to handle different disease types and assessment requirements. Each model is specialized but the overall system can uniformly apply to various rare diseases through the coordinated operation of these models.
3Measurement precision
If comprehensive genome analysis is performed to identify all variants, then diagnostic accuracy is improved, but analysis time and computational resources increase
Solution Approach 1:
The patent segments variant analysis into hierarchical stages: first determining if variants are known or unknown, then classifying coding vs non-coding variants, and finally assessing functional impact only for relevant variants. This segmented approach maintains comprehensive detection accuracy while significantly reducing analysis time by applying detailed analysis only where necessary.
Solution Approach 2:
The system applies partial action by performing comprehensive genome sequencing on all patients but applying detailed functional analysis only to variants that pass through multiple filtering stages. This ensures no critical variants are missed while avoiding wasteful computation on clearly benign variants.
Data Source
AI summary
An AI-based system and method for determining medical therapies is disclosed. The AI-based method includes receiving patient multiomics raw data, extracting one or more reports from the patient multiomics raw data, and detecting one or more genome variants in the patient. Further, the AI-based method includes determining if the one or more genome variants are one or more known variants or one or more unknown variants, determining if the one or more genome variants are one or more coding variants or one or more non-coding variants, and determining if the patient is suffering from a functional loss or a functional excess. The AI-based method includes generating one or more rescue recommendations, determining one or more medical therapies for the patient, and outputting the one or more rescue recommendations and the one or more medical therapies to one or more electronic devices associated with the user.