Multi-Omics Framework Using Temporal Graph Networks for Disease Onset
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Solution Overview
Problem
Existing machine learning systems fail to accurately predict the temporal onset of diseases with significant genetic risk components, relying on static graph representations that lack temporal information and unable to estimate the approximate time of onset.
Innovation Solution
A multi-omics framework utilizing dynamic multigraph data objects and temporal graph networks (TGN) to generate risk window predictions by integrating whole-genome sequence, transcriptome, and clinical event data, enabling the tracking of differential expression over time to estimate the onset of diseases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static graph representations are used for disease prediction, then the system structure is simple, but the temporal prediction accuracy is poor
Solution Approach 1:
The patent transforms static graph representations into dynamic multigraph data objects that evolve over time. The system captures temporal dynamics by representing disease progression as a sequence of graph states, where nodes and edges change based on longitudinal omics data. This dynamic representation enables accurate temporal prediction of disease onset while maintaining manageable system complexity through structured evolution rules.
Solution Approach 2:
The patent adds a temporal dimension to traditional static graph representations. By introducing time as a fourth dimension alongside the existing network structure dimensions, the system can model how disease-related interactions evolve over time. This dimensional extension enables the system to distinguish between acute and chronic disease states and predict temporal onset patterns that were previously invisible to static models.
2Reliability
If multi-omics data integration is implemented, then the prediction capability is improved, but the data processing complexity increases
Solution Approach 1:
The patent segments the multi-omics data processing into distinct modular components: whole-genome sequence processing, transcriptome processing, and clinical event processing. Each omics layer is processed independently through dedicated algorithms that generate standardized representations, which are then integrated into the dynamic multigraph framework. This segmentation reduces overall processing complexity by allowing parallel computation and independent optimization of each module.
Solution Approach 2:
The patent introduces dynamic multigraph data objects as intermediary structures that mediate between raw multi-omics data and final predictions. These multigraph objects serve as a standardized intermediate representation that consolidates information from multiple omics layers, enabling efficient integration and downstream analysis. The intermediary structure transforms complex multi-dimensional data into a unified format that can be processed by the temporal graph network without requiring direct manipulation of raw data.
3Loss of information
If dynamic multigraph data objects are generated, then the temporal information is captured, but the computational resources required increase
Solution Approach 1:
The patent performs preliminary processing of omics data to extract and pre-compute temporal patterns before they are integrated into the dynamic multigraph framework. By pre-processing longitudinal data to identify temporal signatures and progression patterns in advance, the system reduces the computational burden during the main prediction task. This preliminary action enables the system to capture temporal information efficiently without requiring excessive computational resources during real-time prediction.
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.