Hypergraph Classification for Attention-Based Therapeutic Gene Discovery
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Solution Overview
Problem
Existing deep learning methods for identifying therapeutic genes are costly, time-consuming, and dependent on prior knowledge, failing to effectively capture complex biological networks and one-to-many relationships between biological entities.
Innovation Solution
A deep hypergraph learning model that employs structures of genes, ontologies, and phenotypes, along with attention-based learning to capture complex relationships, reducing dependence on specialized knowledge and efficiently expressing multidimensional relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional deep learning methods are used for gene-disease association analysis, then basic gene-disease predictions can be made, but they fail to capture complex one-to-many relationships and require extensive prior knowledge
Solution Approach 1:
The patent transitions from traditional graph structures (one-to-one relationships) to hypergraph structures (one-to-many relationships) by introducing hyperedges that can connect multiple nodes simultaneously. This dimensional change in the data structure enables the model to naturally represent complex biological relationships where a single gene can be associated with multiple diseases and vice versa, without requiring extensive prior knowledge encoding.
Solution Approach 2:
The hypergraph learning framework provides a universal structure that can handle multiple types of biological relationships (gene-gene, gene-disease, disease-disease associations) within a single model architecture. The attention mechanism further enhances universality by dynamically adapting to different relationship patterns in the data, making the model versatile across various biological network types without requiring separate specialized models.
2Adaptability or versatility
If meta-path based approaches are used to define molecular relationships, then one-to-many relationships can be captured, but they require specialized knowledge and significant time and money
Solution Approach 1:
The hypergraph learning model with attention mechanisms performs self-service by automatically learning the importance weights of different relationships directly from the data. Instead of requiring researchers to manually design meta-paths based on specialized knowledge, the model autonomously identifies and weights the relevant biological relationships during training, significantly reducing the time and expertise required while maintaining the ability to capture one-to-many relationships.
Solution Approach 2:
The patent changes the fundamental parameters of the relationship representation from fixed meta-path definitions to dynamic attention weights. These attention parameters are learned automatically during training and can adapt to different biological contexts, eliminating the need for manual meta-path configuration while preserving the ability to represent complex one-to-many relationships flexibly.
3Reliability
If experimental trials are conducted to identify therapeutic genes, then accurate therapeutic targets can be discovered, but the process is expensive and time-consuming
Solution Approach 1:
The hypergraph learning model performs preliminary computational analysis to predict gene-disease associations and identify potential therapeutic targets before experimental trials are conducted. By pre-screening genes using the learned hypergraph relationships and attention-based rankings, the system prioritizes the most promising candidates for experimental validation, thereby reducing the overall time and cost while maintaining high reliability in target identification.
Solution Approach 2:
The patent introduces a computational hypergraph learning system as an intermediary between existing gene-disease data and experimental validation. This intermediary model processes and integrates multiple data sources to generate predictive rankings of therapeutic candidates, serving as a bridge that reduces the need for exhaustive experimental testing while maintaining accurate identification of therapeutic targets.
Data Source
AI summary
Provided are a classification apparatus and a method using a hypergraph for discovery of a therapeutic gene. The apparatus includes a processor and a memory operatively connected to the processor, and the memory stores instructions that, when executed, cause the processor to identify a first hypergraph related to a first target, identify first target embedding based on the first hypergraph, identify a second hypergraph including a part of the first target embedding and related to a second target, identify a second target embedding based on the second hypergraph, identify at least one integrated pair based on the second target embedding, and classify at least one integrated pair based on at least one criterion. The at least one criterion may include unrelated, a biomarker, and a therapeutic gene.