Multi-level Pattern Recognition for Biological Data Clustering
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Solution Overview
Problem
Current genomic technologies for disease prognosis rely on a single, static genomic signature, failing to capture the complete genomic profile of individuals and disregarding additional genomic information, making them ineffective in analyzing newly developing disease subtypes due to their rigid reliance on specific gene subsets.
Innovation Solution
A multi-level, hierarchical architecture that variably selects different subsets of biological data as predictive features and performs iterative clustering based on membership values, enabling adaptive evaluation of various biological data types and reducing reliance on fixed gene sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single, fixed set of genes is used for genomic analysis, then the method is simple and easy to implement, but it cannot adapt to newly developed disease subtypes due to evolving genomic characteristics
Solution Approach 1:
The patent divides the genomic analysis into multiple hierarchical levels: Level 1 performs initial clustering on the full gene set, Level 2 performs clustering on selected subsets of genes, and Level 3 performs final clustering on membership values. This segmentation allows the system to handle both the full complexity of genomic data and adapt to evolving disease subtypes by selectively analyzing different gene subsets at different levels.
Solution Approach 2:
The patent implements dynamic adaptability by allowing the system to flexibly select different subsets of genes at Level 2 based on the specific disease context and available data. This dynamic selection enables the system to adapt to newly developed disease subtypes while maintaining computational efficiency through the hierarchical structure.
2Measurement precision
If all available biological data is analyzed, then the diagnostic accuracy is improved, but the computational time and processing complexity increase
Solution Approach 1:
The hierarchical clustering approach segments the analysis into distinct levels where Level 1 processes the full data set to identify broad patterns, Level 2 processes selected gene subsets to refine cluster identification, and Level 3 processes membership values for final classification. This segmentation enables comprehensive analysis of all biological data while managing computational complexity through systematic division of the processing task.
Solution Approach 2:
Level 1 performs preliminary clustering on the complete biological data set to identify major disease subtypes and patterns before proceeding to more detailed analysis. This preliminary action allows the system to capture the full diagnostic information available in the data while preparing a structured foundation for subsequent refinement steps, thereby optimizing the balance between comprehensive analysis and computational efficiency.
3Reliability
If multiple subsets of biological data are evaluated, then the ability to detect evolving disease patterns is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the evaluation of multiple biological data subsets into a hierarchical structure where Level 1 evaluates the full data set, Level 2 evaluates selected gene subsets, and Level 3 evaluates membership values. This segmentation enables reliable detection of evolving disease patterns by systematically analyzing different data subsets at appropriate levels of granularity while managing system complexity through the organized multi-level architecture.
Solution Approach 2:
The patent applies local quality by selecting specific subsets of genes at Level 2 that are most relevant to particular disease contexts or biological pathways. This allows the system to focus computational resources on the most informative data subsets locally, improving detection accuracy for specific disease subtypes while managing overall system complexity through targeted rather than exhaustive analysis.
Data Source
AI summary
Methods, systems and apparatus for detecting patterns in constituents of at least one biological organism are disclosed. In accordance with one method, clusters of the constituents are determined by selecting different subsets of at least one of genes or proteins and identifying the clusters from biological data corresponding to the selected subsets. Here, membership values for the constituents, indicating membership within the clusters, are calculated for use as a basis of an additional cluster determination process to obtain final clusters of constituents. By underpinning the preliminary clustering on different subsets of biological data and formulating the higher-level clustering on the basis of the membership values, the embodiments can enable an evaluation of a large variety of biological data in a practical, accurate and highly efficient manner.