Multi-Omics Framework Using Temporal Graphs for Disease Onset Windows
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Solution Overview
Problem
Current machine learning systems fail to accurately predict the onset time of diseases with significant genetic risk components due to the lack of temporal information in static graph representations and reliance on static risk scores, leading to inadequate disease onset estimation.
Innovation Solution
A multi-omics framework utilizing dynamic multigraph data objects generated from whole-genome sequence, transcriptome, and clinical event data, trained with a temporal graph network (TGN) to predict a risk window for disease onset by capturing temporal information through evolving graph representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static graph representations and static risk scores are used, then the system is simple and easy to operate, but the prediction accuracy of disease onset time is poor due to lack of temporal information
Solution Approach 1:
The patent transforms static graph representations into dynamic multigraph data objects that evolve over time. The temporal graph network processes these dynamic structures to capture temporal information and trajectories, enabling accurate prediction of disease onset time while maintaining manageable system complexity through structured data transformations
Solution Approach 2:
The patent adds a temporal dimension to static risk scores and graph representations. By incorporating time-series transcriptome data and clinical event information, the system transitions from static to dynamic representations, enabling prediction of disease onset time with improved precision through multi-omics integration
2Measurement precision
If multi-omics framework with dynamic multigraph data objects is used, then the prediction accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex prediction task into distinct modules: multi-omics data integration module, dynamic multigraph construction module, temporal graph network prediction module, and risk window estimation module. This segmentation manages framework complexity by breaking down the multi-omics processing into manageable, specialized components
Solution Approach 2:
The patent introduces dynamic multigraph data objects as intermediary structures that bridge raw multi-omics data and final predictions. These intermediary representations standardize the complex data formats from genomics, transcriptomics, and clinical data, simplifying the overall framework by providing a unified representation layer
3Measurement precision
If temporal information is captured through evolving graph representations, then the estimation of risk window is precise, but the data processing complexity increases
Solution Approach 1:
The patent performs preliminary data processing by transforming raw multi-omics data into standardized dynamic multigraph data objects before feeding them to the temporal graph network. This preliminary action pre-organizes the data structure, reducing the processing burden during prediction and improving risk window estimation precision
Solution Approach 2:
The patent changes data parameters by converting static risk scores into dynamic representations that include temporal information. By transforming data from static to dynamic formats and adjusting parameter representations, the system captures temporal trajectories without proportionally increasing processing complexity
Data Source
AI summary
Methods, apparatuses, systems, computing devices, computing entities, and/or the like are provided. An example method may include selecting at least one client profile data object from a plurality of client profile data objects; retrieving at least one initial transcriptome data object and at least one subsequent transcriptome data object associated with the at least one client profile data object; generating at least one dynamic multigraph data object based at least in part on the at least one initial transcriptome data object, the at least one subsequent transcriptome data object, and at least one clinical event data object; training a temporal graph network based at least in part on the at least one dynamic multigraph data object to generate a risk window prediction data object; and performing at least one data operation based at least in part on the risk window prediction data object.