Multimodal Graph for Precision Medicine Drug Scoring
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Solution Overview
Problem
Current precision medicine approaches, particularly in pharmacogenomics, primarily focus on single mutations or tumour mutational burden, neglecting valuable information from other omics modalities and domain knowledge, which may impact their effectiveness.
Innovation Solution
A medical information processing apparatus and method that integrates drug target information, protein-protein interaction data, and multimodal omics data to construct a graph, allowing for the generation of personalized drug scores based on patient-specific omics data and domain knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current precision medicine approaches focus on single mutations or tumour mutational burden, then the analysis is simple and actionable, but the treatment effectiveness is limited and only works for a small subset of patients
Solution Approach 1:
The patent merges multiple omics modalities (genomics, transcriptomics, proteomics, metabolomics) with domain knowledge into a unified graph representation. This integration allows the system to capture complex biological pathways and interactions that single-mutation approaches miss, thereby improving treatment effectiveness for a broader patient population while managing complexity through structured data organization.
Solution Approach 2:
The system creates a universal graph framework that can accommodate various types of omics data and domain knowledge in a single integrated structure. This multi-functional approach enables the same framework to handle different data types and application scenarios, improving reliability across diverse patient cases without requiring separate analysis pipelines for each modality.
2Measurement precision
If multimodal omics data and domain knowledge are integrated into a graph, then treatment precision is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex integration task into manageable components: constructing the graph from domain knowledge, mapping omics data to graph nodes, and performing precision calculations. This segmentation allows each component to be optimized independently and simplifies the overall computational process while maintaining high treatment precision through systematic integration.
Solution Approach 2:
The graph structure serves as an intermediary that mediates between raw omics data and treatment recommendations. It organizes and contextualizes complex multi-omics information, making the computational process more manageable and enabling precise treatment predictions through structured relationships rather than direct complex calculations.
3Loss of information
If only single mutations are considered in precision medicine, then the clinical pipeline remains simple and actionable, but valuable information from other omics modalities is lost
Solution Approach 1:
The patent embeds multiple levels of information within the graph structure: single mutations are represented as nodes, which are connected to broader pathways, cellular processes, and phenotypic outcomes. This nesting allows the system to maintain simplicity for individual mutation analysis while simultaneously capturing and preserving valuable information from multiple omics modalities through hierarchical relationships.
Data Source
AI summary
A medical information processing apparatus comprising a processing circuitry configured to: receive drug target information based on a medical condition, the drug target information comprising at least one target biomolecule each associated with a respective drug suitable for the treatment of said medical condition; receive protein-protein interaction information based on domain knowledge, receive multimodal omics data for at least one subject; construct a graph based upon the drug target information, the protein-protein interaction information and the multimodal omics data; and generate, for each of the at least one drug, a score for each of the at least one subject based on the graph and the multimodal omics data. For each of the at least one subject, the drugs may be ranked based on their respective generated score.