Musical Orchestral Algorithm for Gene Expression Classification
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Solution Overview
Problem
Current gene expression analysis methods for predicting treatment responses in diseases face challenges with low prediction accuracy, especially with small sample sizes, and increasing sample size or incorporating additional data types like copy number variation and SNPs increases costs and complexity.
Innovation Solution
A mathematical musical orchestral algorithm (MMOA) is used to convert gene expression data into sound frequency patterns, allowing for the classification of patients as responders or non-responders to treatment by analyzing tissue samples, which can be manually operated or converted into a web-based program for sound frequency pattern generation and recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gene expression data analysis methods are used to predict treatment responses, then treatment response prediction is achieved, but prediction accuracy is low with small sample sizes
Solution Approach 1:
The patent transforms gene expression data into musical parameters (frequency, rhythm, melody) through the MMOA algorithm. This parameter transformation allows the system to capture patterns in a different representation, enabling high prediction accuracy with small sample sizes by leveraging the mathematical structure of musical patterns rather than relying on large quantities of biological data samples.
Solution Approach 2:
The patent replaces conventional statistical mechanical analysis methods with a mathematical musical orchestral algorithm that uses music theory and acoustic mathematics. This substitution allows for more efficient pattern recognition in small datasets by using the structured nature of musical compositions to encode biological data patterns.
2Measurement precision
If sample size is increased to improve prediction accuracy, then prediction accuracy is improved, but cost and complexity increase
Solution Approach 1:
By transforming gene expression data into musical parameters, the system reduces the dimensionality and complexity of the data while preserving essential patterns. This parameter transformation enables high prediction accuracy without requiring large sample sizes, thereby reducing the complexity of data collection and analysis requirements.
3Measurement precision
If multiple data techniques (gene expression, copy number variation, SNP) are used to increase prediction accuracy, then prediction accuracy is improved, but test cost increases
Solution Approach 1:
The patent extracts and transforms only the gene expression data into musical parameters, separating this from the need for additional data types. By taking out the essential patterns from gene expression data alone and representing them musically, the system achieves high prediction accuracy without requiring costly supplementary tests for copy number variation or SNP analysis.
Data Source
AI summary
Methods for classifying patients as responders or non-responders to treatment of a disease and predicting recurrence of disease in a patient using audio tunes are provided.