MicroRNA Expression Visualization for Diagnostic Grounds
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Solution Overview
Problem
Current technologies can determine the existence or nonexistence of diseases based on microRNA biomarkers but fail to provide the grounds for such determinations.
Innovation Solution
An image generation device that converts microRNA expression level data into two-dimensional image-rendition data, using methods like Levenshtein distance for assignment, and generates contribution-presentation images to classify health conditions into healthy, disease, and pre-symptomatic classes, allowing for visualization of the grounds of classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If microRNA expression level data is converted into two-dimensional image-rendition data and contribution-presentation images are generated, then the grounds for health condition determination can be visually indicated, but the device complexity increases
Solution Approach 1:
The patent creates a two-dimensional image copy of the microRNA expression data matrix, where each pixel represents expression levels of specific microRNA types. This visual copy preserves the grounds information in an intuitive format without requiring complex additional hardware, as the image generation can be achieved through software processing of the existing data matrix.
Solution Approach 2:
The patent transforms one-dimensional microRNA expression data into a two-dimensional visual representation. By mapping microRNA types to spatial coordinates based on sequence similarity (using Levenshtein distance), the system adds a spatial dimension that enables visual interpretation of the grounds for determination while maintaining the underlying data structure.
2Ease of operation
If microRNA types are assigned to matrix elements according to Levenshtein distance for sequence similarity, then the visualization of grounds becomes more intuitive, but the calculation complexity increases
Solution Approach 1:
The patent changes the parameter for organizing microRNA types from arbitrary indexing to Levenshtein distance-based spatial positioning. By calculating the edit distance between microRNA sequences and using this as a coordinate system, the system creates an intuitive visual layout where similar sequences are spatially proximate, making the grounds for determination easier to interpret despite the computational effort.
3Adaptability or versatility
If classification is performed into multiple classes (healthy, disease, pre-symptomatic), then the diagnostic capability is improved, but the measurement precision requirements increase
Solution Approach 1:
The patent segments the diagnostic space into multiple distinct classes (healthy, disease, pre-symptomatic) based on microRNA expression patterns. By dividing the continuous expression data into discrete classification categories, the system enhances diagnostic versatility while managing precision requirements through the visual contribution maps that highlight which microRNAs drive each classification decision.
Data Source
AI summary
An image generation device includes an imaging unit configured to convert data representing an expression level for each microRNA type into image-rendition data serving as data representing a matrix of two dimensions or more, a classification unit configured to perform classification of the image-rendition data, and a contribution-presentation-image generation unit configured to generate a contribution-presentation image representing a contribution of a specific part of the image-rendition data to the classification.