Biological Data Analysis Ontology Integration
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Solution Overview
Problem
Modern molecular biology generates large data sets, but existing solutions for data visualization and interpretation, such as Microsoft Excel, are inadequate, and purpose-built software often fails to integrate biological data with external information sources effectively.
Innovation Solution
A computer system with a data analysis module that combines user-supplied biological data with an ontological database, using a graphical user interface (GUI) for genomic data visualization, featuring data and feature icons that represent biological data points and properties, allowing filtering and customization of visual metrics for enhanced interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If purpose-built software is used for biological data analysis, then data analysis capability is improved, but integration with external information sources deteriorates
Solution Approach 1:
The software system integrates multiple functions including data import, ontological database integration, data analysis, and visualization capabilities into a single platform. The system can process various biological data types (gene expression, proteomics, metabolomics) and integrate with multiple external databases (Gene Ontology, KEGG, Reactome), making it a universal tool that eliminates the need for separate specialized software for each function.
Solution Approach 2:
The system employs an ontological database as an intermediary layer between user data and external information sources. This mediator enables seamless integration by mapping user data to standardized ontological terms, which then connect to multiple external databases through the same interface, solving the integration problem while maintaining specialized analysis capabilities.
2Ease of operation
If general-purpose programs like Microsoft Excel are used, then ease of operation is improved, but data interpretation capability deteriorates
Solution Approach 1:
The system uses color-coded visualizations to represent different data attributes and analysis results. Data points are colored based on their properties (e.g., up-regulated vs down-regulated genes, different pathway enrichments), making complex biological data interpretable through intuitive visual cues that maintain ease of operation while enhancing interpretation capability.
Solution Approach 2:
The system transforms tabular biological data into multi-dimensional visual representations including scatter plots, pathway maps, and network graphs. This dimensional transformation allows users to interpret complex relationships and patterns in biological data through visual spatial arrangements rather than traditional spreadsheet views, enhancing interpretation while maintaining user-friendly interaction.
3Quantity of substance
If large biological data sets are analyzed, then data completeness is improved, but interpretation difficulty deteriorates
Solution Approach 1:
The system segments large biological datasets into meaningful functional groups based on ontological categories (pathways, processes, molecular functions). Instead of presenting all individual data points, the system groups genes by their involvement in specific biological pathways or processes, making large datasets interpretable through hierarchical organization that maintains data completeness while reducing interpretation complexity.
Solution Approach 2:
The ontological database serves as an intermediary that bridges raw biological data and human interpretation. By mapping data to standardized ontological terms and relationships, the system automatically organizes and annotates large datasets with biological context, enabling interpretation of comprehensive data without overwhelming the user with raw information.
Data Source
AI summary
The invention provides computer systems and methods for visualization and analysis of relationships between biological data.