Automated Gene Panel Design via DisGeNET Scoring
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Solution Overview
Problem
Developing robust, high-performing targeted next-generation sequencing panels that effectively prioritize genes and regions for specific diseases is challenging due to the need for extensive expert efforts and lacks automation in gene selection processes.
Innovation Solution
A system comprising a disease association database, gene scoring algorithm, and virtual panel library that uses graph database architecture and DisGeNET scores to identify and rank gene-disease associations, automating the gene selection process for gene panel design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional expert-driven approaches are used for gene selection, then gene panel accuracy can be maintained, but the process requires tremendous expert effort and time
Solution Approach 1:
The patent introduces a bioinformatics engine as an intermediary between available genomic data and expert gene panel design. This engine automatically processes disease-associated gene data, applies filtering and prioritization algorithms, and generates ranked gene lists, thereby mediating the complex data processing tasks and reducing the time experts need to invest while maintaining selection accuracy
Solution Approach 2:
The system enables self-service by allowing the bioinformatics engine to autonomously perform gene selection tasks without requiring continuous expert intervention. The automated pipelines can independently retrieve data, apply analysis methods, and generate gene panel recommendations, making the system self-sufficient for routine gene selection while experts only need to review and validate results
2Reliability
If comprehensive gene coverage is pursued for all relevant genes, then diagnostic completeness improves, but panel complexity and difficulty in identifying all relevant genes increases
Solution Approach 1:
The patent applies segmentation by dividing the comprehensive gene set into prioritized groups based on disease association strength and clinical relevance. The bioinformatics engine ranks genes and allows selection of top-N genes or genes meeting specific threshold criteria, thereby segmenting the overwhelming complete gene list into manageable, clinically actionable panels that maintain diagnostic completeness for high-priority genes while reducing overall panel complexity
Solution Approach 2:
The system enables dynamic adjustment of gene selection parameters such as minimum disease association score thresholds, maximum panel size, and disease specificity criteria. By changing these parameters, users can optimize the balance between diagnostic completeness and panel complexity depending on specific clinical needs, generating different panel configurations from the same comprehensive data
3Productivity
If automated bioinformatics engines are implemented for gene selection, then expert effort is reduced and scalability improves, but the complexity of the analysis pipeline increases
Solution Approach 1:
The patent implements a universal bioinformatics engine that performs multiple functions within a single integrated platform: data retrieval from various sources, quality control filtering, gene-disease association analysis, gene ranking and prioritization, and panel design generation. This multi-functional approach consolidates what would otherwise require separate tools and expert coordination, improving scalability while managing pipeline complexity through integration
Solution Approach 2:
The system incorporates feedback mechanisms where the bioinformatics engine provides structured output data and performance metrics that can be reviewed and adjusted by experts. This feedback loop allows the automated pipeline to be validated, refined, and optimized based on real-world performance, making the complexity transparent and manageable while maintaining high productivity
Data Source
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AI summary
A system and method of selecting genes for a gene panel, includes retrieving gene-disease associations of genes associated with diseases at a given level in the disease hierarchy from a disease association database. The disease association database stores disease information, gene information, phenotype information, associations between diseases in the disease hierarchy, gene-disease associations and strength parameters related to the gene-disease associations. For each gene associated with the diseases at the given level, the strength parameters are weighted and combined to determine a rank score for the each gene. The genes are ranked based on the rank scores to provide ranked gene information. The ranked gene information is linked with diseases at the higher levels of the disease hierarchy based on hierarchical relationships. The ranked gene information for gene-disease associations can be used to select genes for a gene panel design.