Meta-Analysis Infrastructure for Biomedical Data Integration
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Solution Overview
Problem
Researchers face challenges in efficiently integrating and analyzing vast amounts of diverse biomedical data from various sequencing technologies, assays, and organisms, necessitating fast and efficient tools to assimilate new information and connect it with existing data across different platforms.
Innovation Solution
A meta-analysis infrastructure is developed to combine orthogonal data types, including mutation, methylation, and gene expression profiling, to elucidate mechanisms of disease development and treatment responses, enabling the integration of sequence-centric data with gene-centric information and public knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If researchers manually integrate and analyze diverse biomedical data from various sequencing technologies and assays, then comprehensive analysis coverage is achieved, but time consumption and analysis efficiency deteriorate
Solution Approach 1:
The system segments diverse biomedical data into standardized feature sets with defined schemas, organizing mutation, methylation, and expression data into separate manageable components that can be processed independently and then integrated efficiently
Solution Approach 2:
The patent introduces an intermediary integration layer that standardizes and harmonizes data from different sequencing technologies and assays before analysis, acting as a mediator that translates diverse data formats into a unified structure for efficient processing
2Adaptability or versatility
If comprehensive data from multiple platforms and organisms is integrated, then analysis completeness is improved, but system complexity increases
Solution Approach 1:
The system implements universal feature set schemas that can accommodate multiple data types, platforms, and organisms through a single standardized framework, enabling the same integration mechanism to handle diverse biomedical data without requiring platform-specific processing logic
Solution Approach 2:
The patent utilizes parameter-based feature definitions where data characteristics are described through standardized parameters and attributes, allowing the system to adapt to different data sources by changing parameter values rather than restructuring the entire integration approach
3Quantity of substance
If large volumes of biomedical data are stored and processed, then information completeness is improved, but computational resource requirements increase
Solution Approach 1:
The system performs preliminary data standardization, validation, and feature extraction during the data ingestion phase, preparing data in advance for analysis. This preliminary processing organizes data into optimized feature sets that reduce computational overhead during subsequent querying and analysis operations
Data Source
AI summary
According to various embodiments, aspects of the invention provide a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from diverse sequencing technologies as well as different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure. The methods, systems and apparatuses described enable combining orthogonal types of data and available public knowledge to elucidate mechanisms governing normal development, disease progression, as well as susceptibility of individuals to disease or response to drug treatments.