Gene Interaction Network Analysis via Relevance Threshold Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional technologies for analyzing gene interaction networks are limited by incomplete databases, requiring manual interpretation and struggling to handle multiple diseases, making efficient discovery and sorting of disease-related genes difficult, especially when dealing with complex and large gene interaction networks.
Innovation Solution
A computer-readable medium and method that sets a threshold for relevance between biological events and gene interactions, detects interactions with relevance equal to or higher than the threshold, and generates partial networks for each biological event, facilitating automated analysis and selection of relevant gene interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual interpretation of gene interaction networks is used, then analysis precision can be maintained, but productivity and ease of operation deteriorate due to time-consuming manual processes
Solution Approach 1:
The system performs automatic relevance calculation and network generation without requiring manual interpretation. The computer automatically calculates relevance between biological events and gene interactions, and generates partial networks based on threshold criteria, enabling the system to serve itself rather than requiring continuous manual operation.
Solution Approach 2:
The patent replaces manual mechanical interpretation processes with automated computational algorithms. The relevance calculation unit automatically computes relevance scores using defined algorithms, and the network generation unit automatically constructs partial networks, substituting human cognitive processing with mechanical computational operations.
2Measurement precision
If complete gene interaction networks are analyzed, then measurement precision is improved, but device complexity increases due to large network size
Solution Approach 1:
The system segments the complete gene interaction network into multiple partial networks based on relevance thresholds. By dividing the large network into smaller, manageable partial networks that are generated automatically based on relevance criteria, the system maintains analysis precision while reducing the complexity of individual network structures that need to be interpreted.
Solution Approach 2:
The system extracts only the relevant portions of the gene interaction network by filtering based on relevance thresholds. The relevance calculation unit identifies and extracts gene interactions that meet the threshold criteria, separating them from irrelevant interactions, thus reducing complexity while preserving measurement precision for the extracted relevant information.
3Ease of operation
If databases are used for gene interaction analysis, then ease of operation is improved, but reliability deteriorates due to incomplete database coverage
Solution Approach 1:
The system introduces an intermediary relevance calculation unit that bridges the gap between available databases and the need for comprehensive analysis. This intermediary component calculates relevance based on multiple criteria and generates partial networks that complement database information, allowing the system to maintain ease of operation while improving reliability through multi-source integration.
Data Source
AI summary
Detailed information of each analysis subject partial network is displayed on a left pane of a screen. The detailed information includes the number of nodes, the number of edges, and accumulative coverage of the analysis subject partial network for each disease. Based on the detailed information, a user can designate the analysis subject partial network of a disease the user wishes to analyze. When the user has designated the disease, a network diagram indicating a partial network related to the designated disease is displayed on a right pane.