Frequency Domain Analysis for Biological Subpopulation Detection
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Solution Overview
Problem
The complexity and volume of genetic profiles make it difficult to efficiently and accurately analyze biological data for detecting subpopulations, such as clonal populations in tumors, using existing bioinformatics methods.
Innovation Solution
Formulating biological data as discrete-time real-valued vector signals and applying frequency domain analysis to obtain spectral properties, which are then used to identify subpopulations through a dissimilarity index and similarity metrics, enabling efficient and accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bioinformatics methods are used to analyze genetic profiles, then the analysis can be performed with existing tools, but the complexity and volume of data make it difficult to efficiently and accurately detect subpopulations
Solution Approach 1:
The patent replaces traditional mechanical/statistical analysis methods with signal processing techniques. Genetic profiles are transformed into discrete-time real-valued vector signals, and frequency domain analysis (spectral analysis) is applied to extract features. This substitution of analytical methodology enables both high accuracy in subpopulation detection and computational efficiency, resolving the contradiction between measurement precision and productivity.
2Reliability
If the volume of genetic profile data is increased to improve detection accuracy, then more comprehensive analysis is possible, but the complexity and computational burden increase
Solution Approach 1:
The patent extracts essential spectral features from comprehensive genetic profile data through frequency domain analysis. By transforming the complete genetic data into spectral properties (frequency domain representation), the method captures the most relevant information for subpopulation detection while discarding redundant data. This extraction process maintains high detection reliability while reducing system complexity and computational burden.
Solution Approach 2:
The patent changes the parameter representation of genetic data from the time domain (raw genetic profiles) to the frequency domain (spectral properties). This parameter transformation reveals underlying patterns and structures in the data that are not apparent in the original form, enabling accurate subpopulation detection with reduced computational complexity. The spectral features serve as transformed parameters that maintain reliability while simplifying analysis.
Data Source
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AI summary
Methods, systems and apparatus for detecting subpopulations of constituents of at least one biological organism are disclosed. In accordance with exemplary embodiments, biological data compiled from a cohort of the constituents of at least one biological organism is formulated (112) as a set of discrete-time real valued vector signals. Further, frequency domain analysis is performed (114) on the vector signals of the biological data to compile spectral properties of the vector signals. The spectral properties can be employed to efficiently detect subpopulations of the cohort while maintaining a high degree of accuracy.