Biological Network Embedding for Topology Change Detection
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Solution Overview
Problem
Current methods for analyzing large and complex Heterogeneous Biological Networks (HBNs) fail to capture deep topology information and quantify changes effectively, limiting the understanding of disease conditions and therapeutic responses.
Innovation Solution
A system and method that construct HBNs, derive sub-networks, determine embedding vectors for each node, and compare these vectors before and after changes to identify and quantify changes using graph theory and database technologies, considering both network structure and functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional topological analysis methodology is used, then the analysis process is simple, but the depth level information about the topology is missing
Solution Approach 1:
The patent transitions from conventional topological analysis to a multi-dimensional embedding space approach. Nodes are represented as vectors in a high-dimensional space where dimensions capture various topological properties (degree, betweenness, closeness, eigen centrality). This dimensional transformation preserves and enriches topological information while enabling more sophisticated analysis capabilities.
Solution Approach 2:
The patent changes the parameter representation from discrete topological metrics to continuous embedding vectors. Each node is transformed into a vector where each dimension represents a specific topological parameter, allowing for nuanced representation and comparison of nodes based on multiple topological characteristics simultaneously.
2Productivity
If lower dimensional embeddings are used to optimize computations, then the computational efficiency is improved, but the complete information on nodes is not captured
Solution Approach 1:
The patent employs a balanced approach by using embeddings with sufficient dimensionality to capture complete node information while remaining computationally tractable. The embedding dimension is chosen to be large enough to preserve topological structure (capturing degree, betweenness, closeness, eigen centrality) but not excessively large to maintain computational efficiency in comparing node pairs.
Solution Approach 2:
The patent transforms node representations into embedding vectors in a carefully selected dimensional space. This dimensional transformation allows the system to capture complete topological information about each node (multiple centrality measures and structural properties) while the embedding formulation itself is designed to be computationally efficient for large-scale network analysis.
3Loss of information
If the large HBN is analyzed to obtain high-detailed information, then the understanding of indirect relationships is improved, but the analysis becomes cost-intensive and time-consuming
Solution Approach 1:
The patent pre-computes embedding vectors for all nodes in the biological network, capturing their topological properties and relationships in advance. These pre-computed embeddings are stored and can be efficiently queried and compared later, avoiding the need for repeated expensive computations when analyzing different hypotheses or relationships.
Solution Approach 2:
The patent uses embedding vectors as compressed representations (copies) of the full topological structure. Instead of analyzing the entire complex network structure repeatedly, the system works with these compact vector representations that capture the essential topological information, enabling fast comparison and analysis of node relationships.
4Device complexity
If conventional methods are used to analyze HBN, then the implementation is straightforward, but the quantification of information flow and changes is not achieved
Solution Approach 1:
The patent replaces conventional qualitative topological analysis with a quantitative vector-based measurement system. By representing nodes as embedding vectors, the system enables precise measurement of information flow and changes through vector operations (comparisons, distances, angles), transforming the analysis from mechanical counting of edges to sophisticated quantitative measurement of topological properties.
Data Source
AI summary
A system for identifying one or more changes in a Biological Network, comprising a processor configured to construct a Heterogeneous Biological Network that comprises a plurality of nodes and a plurality of edges. The processor is configured to derive one or more sub-networks from the constructed HBN, and determine an embedding vector for each node of each sub-network. The processor is configured to identify one or more changes in each sub-network by comparing the embedding vector of each node of in a respective sub-network before and after an input action associated with a change in at least one sub-network and determine a plurality of scores for each node of each sub-network based on a pre-defined set of parameters. The processor is configured to identify the one or more changes in the BN based on the determined plurality of scores for each node in each sub-network.