Interactive Visualization of Clinical and Genetic Data Associations
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Solution Overview
Problem
Clinicians face difficulties in efficiently utilizing clinical and genetic data to determine relevant genes for observed phenotypes, as the vast amount of available data makes it challenging to identify meaningful insights for personalized treatment decisions.
Innovation Solution
A computerized method that quantifies the association between phenotypes and genes, creating an interactive graphical visualization by receiving clinical data, determining associations with disorders and genetic properties, and generating a graphical user interface that allows users to interactively sort and filter data, facilitating the identification of relevant genetic properties for personalized treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If clinicians access comprehensive genetic databases (e.g., OMIM with 7,500 disorders), then the completeness of genetic information increases, but the complexity of data analysis and the difficulty of identifying relevant genes increase significantly
Solution Approach 1:
The patent segments the overwhelming genetic data by creating phenotype-specific gene panels. Instead of presenting all 7,500 disorders and their associated genes, the system divides the data into manageable subsets based on observed phenotypes, allowing clinicians to focus on relevant gene groups rather than the entire database.
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a mediator between the comprehensive genetic database and the clinician. This intermediary automatically queries databases, integrates data from multiple sources, and presents processed results, shielding the clinician from the raw complexity of the underlying data structures.
2Measurement precision
If clinicians manually analyze vast amounts of genetic data to identify relevant genes, then the accuracy of gene selection may improve, but the time required for diagnosis and treatment decision-making increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing and pre-organizing gene associations with phenotypes before the clinician needs them. The system maintains pre-established relationships between phenotypes and genes, so when a clinician inputs observed phenotypes, the relevant gene panels are immediately retrieved and presented without requiring real-time manual analysis.
Solution Approach 2:
The patent implements feedback mechanisms where the system learns from clinician interactions with the gene panels. By monitoring which genes and phenotypes clinicians focus on, the system refines its future recommendations, improving accuracy over time while maintaining rapid response times.
3Loss of information
If the system presents all potential genetic associations for observed phenotypes, then the comprehensiveness of results increases, but the ease of operation and interpretability for clinicians decreases
Solution Approach 1:
The patent applies local quality by providing different levels of information detail in different parts of the interface. The system presents a simplified overview of gene panels at the top level for quick interpretation, while allowing clinicians to drill down into detailed genetic associations and pathway information when needed, ensuring both ease of operation and comprehensiveness are satisfied at appropriate levels.
Data Source
AI summary
This disclosure relates to generating interactive graphical visualisations of clinical and genetic data. A processor receives the clinical data indicative of observed phenotypes, accesses a database to determine associations of the observed phenotypes with disorders and accesses a second database to determine associations between the disorders and genetic properties. The processor then determines an association value for each combination of the observed phenotypes and the genetic properties based on a number of paths between them. The processor also generates a graphical user interface, comprising an arrangement of the association values and a user control element associated with the phenotypes and/or the genetic properties. Finally, upon detecting user interaction in relation to the user control element, the processor re-arranges the arrangement of the association values in the graphical user interface to reflect the detected user interaction.