Biomedical Data Meta-Analysis Infrastructure for Cross-Platform Querying
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Solution Overview
Problem
Researchers face challenges in quickly assimilating and integrating vast amounts of biomedical data from diverse sources and platforms, needing efficient tools to navigate and analyze information across different biological and chemical assays, organisms, and data types.
Innovation Solution
A meta-analysis infrastructure is developed, including a Knowledge Base that captures, organizes, and queries large-scale data through Feature Sets, Feature Groups, Scoring Tables, and Index Sets, allowing for efficient data import, preprocessing, and correlation scoring to support user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If researchers manually assimilate and integrate biomedical data from diverse sources, then data accuracy and contextual understanding are maintained, but time consumption and research efficiency deteriorate
Solution Approach 1:
The system segments biomedical data into structured components including Feature Sets (individual data elements with metadata), Feature Groups (collections of related features), and Study Sets (aggregated experimental data). This segmentation enables automated processing while maintaining data integrity and contextual relationships, resolving the contradiction between automation efficiency and data accuracy.
Solution Approach 2:
The patent introduces an intermediary indexing system that automatically generates unique identifiers, synonyms, and cross-references for biomedical features. This intermediary layer enables fast automated querying and integration across diverse data sources without requiring manual data assimilation, thereby improving research efficiency while preserving data accuracy through structured mediation.
2Adaptability or versatility
If comprehensive biomedical data from multiple platforms and organisms is integrated, then data completeness and analytical capability are improved, but system complexity and data integration difficulty worsen
Solution Approach 1:
The system implements a universal Feature Set structure that can represent diverse biomedical data types (genomic, proteomic, metabolomic) from multiple organisms and experimental platforms using a common schema. This universality enables integration of heterogeneous data sources without proportionally increasing system complexity, as the same structural framework handles multiple data types and sources.
Solution Approach 2:
The patent employs parameter-based feature representation where biomedical data elements are characterized by standardized parameters (feature identifiers, synonyms, genomic coordinates, experimental conditions). By changing the representation from unstructured diverse formats to standardized parameter sets, the system achieves high adaptability across data types while managing complexity through consistent parameter schemas.
3Speed
If large-scale biomedical data is stored and queried without structured organization, then storage simplicity is maintained, but data retrieval speed and analysis efficiency deteriorate
Solution Approach 1:
The system performs preliminary organization of biomedical data into Feature Sets, Feature Groups, and Study Sets during data import, automatically generating indexes, unique identifiers, and metadata structures. This preliminary action enables fast retrieval operations later without requiring complex query-time processing, achieving high retrieval speed while managing organization complexity through upfront structuring.
Solution Approach 2:
The patent creates simplified copy structures (Feature Sets as derived data sets from raw data) that capture essential information from complex original data sources. These copied structures with standardized formats enable rapid querying and analysis without requiring access to the full complexity of the original diverse data formats, thereby improving retrieval speed while managing organizational complexity.
Data Source
AI summary
The present invention relates to methods, systems and apparatus for capturing, integrating, organizing, navigating and querying large-scale data from high-throughput biological and chemical assay platforms. It provides a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure.