Gene Ranking via Multi-Source Data Integration
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Solution Overview
Problem
Current methods for identifying disease-specific genes are time-consuming and inefficient, often requiring months of review and analysis across various sources, and conventional pathway-based gene ranking provides limited value in complex disease scenarios.
Innovation Solution
A system and method for selecting and ranking genes associated with a given phenotype using curated gene regulation data, interactome data, and in silico gene scores, which integrates experimental and in silico data to identify genes potentially associated with biological, chemical, or medical concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional pathway-based gene ranking methods are used, then analysis can be performed using existing data sources, but the identification process is time-consuming and provides limited value in complex disease scenarios
Solution Approach 1:
The patent segments the gene identification process into multiple independent scoring components: experimental data scores, in silico scores, curated database scores, and network-based scores. Each component evaluates genes independently across different data dimensions, allowing parallel processing and rapid computation while maintaining comprehensive assessment through aggregation of all score types.
Solution Approach 2:
The patent merges multiple diverse data sources and scoring methodologies into a unified gene ranking framework. It integrates experimental data, computational predictions, curated knowledge bases, and network interactions into a composite scoring system that provides both speed through automated integration and reliability through multi-source validation.
2Measurement precision
If comprehensive data from multiple sources is integrated, then gene identification accuracy improves, but data processing complexity and time increase
Solution Approach 1:
The patent segments the complex data integration system into modular scoring modules, each handling a specific data source or methodology. This segmentation allows independent optimization of each module and simplifies the overall system architecture by breaking down the complex integration task into manageable, standardized components that can be processed in parallel.
Solution Approach 2:
The patent transforms heterogeneous data from multiple sources into a standardized parameter format (gene scores). By converting diverse data types into a common scoring metric, the system simplifies data integration while maintaining the precision benefits of comprehensive data analysis, allowing straightforward aggregation and comparison across all data sources.
3Loss of information
If manual review and analysis of various sources is performed, then comprehensive gene sets can be identified, but the process takes months or longer
Solution Approach 1:
The patent replaces manual mechanical review processes with automated computational systems. The scoring framework automatically integrates data from multiple sources, performs comprehensive gene evaluation, and generates ranked results without human intervention, maintaining complete information assessment while reducing analysis time from months to minutes or hours through algorithmic processing.
4Adaptability or versatility
If conventional methods are used, then existing data sources can be analyzed, but the ability to identify disease-specific genes in complex diseases is limited
Solution Approach 1:
The patent creates a universal scoring framework that can be applied to any disease or biological concept by simply changing the input data and scoring parameters. The multi-component scoring system is designed to handle diverse data types and disease contexts, providing both broad adaptability across different diseases and precise disease-specific gene identification through customized data integration and scoring weights.
Data Source
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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. According to various embodiments, methods, systems and interfaces for identifying genes that are potentially associated with a biological, chemical or medical concept of interest.