Persistent Betti Curve Classification for Multi-Modal Phenotypes
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Solution Overview
Problem
Existing methods struggle to integrate across multiple data modalities and learn complex features in topological structures for accurate phenotype classification, particularly in diagnosing and predicting patient diseases.
Innovation Solution
A computer-implemented method utilizing persistent Betti curve analysis and algebraic topology to split and partition data, train a model on Betti curves, and iteratively re-train based on determination accuracies for phenotype classification, enabling high-dimensional feature exploitation and diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis methods are used for phenotype classification, then the process is simple and fast, but the classification accuracy is low and cannot effectively handle complex genetic and environmental data
Solution Approach 1:
The patent segments the complex data analysis process into distinct stages: data preprocessing, Betti curve generation, model training, and phenotype classification. Each stage handles specific aspects of the data, making the overall complex task manageable and improving accuracy through systematic processing of complex genetic and environmental data
Solution Approach 2:
The patent introduces Betti curves as an intermediary representation that bridges raw complex data and phenotype classification. The Betti curves capture topological features of data distributions, serving as a mediator that transforms complex multi-modal data into a format suitable for accurate phenotype classification without requiring direct manipulation of the complex original data
2Reliability
If multiple data modalities are integrated for comprehensive phenotype analysis, then the diagnostic accuracy improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent creates a universal framework that handles multiple data modalities (genomic, transcriptomic, proteomic, clinical) through a single consistent process. The Betti curve generation and model training mechanisms work uniformly across different data types, allowing comprehensive multi-modal integration without requiring separate processing pipelines for each data modality, thus improving diagnostic accuracy while managing processing complexity
3Measurement precision
If iterative model training is performed to improve classification accuracy, then the phenotype recognition precision increases, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing data and generating Betti curves before the main iterative training process. The model is trained on pre-transformed data representations, which reduces the computational burden during iterative training and accelerates convergence. This preliminary transformation of data into topological features enables higher precision phenotype recognition with reduced training time compared to working with raw data throughout the entire training process
Data Source
AI summary
A computer-implemented method for data analysis is provided. The computer-implemented method includes splitting data into first and second training data and first and second test data, partitioning the first and second training data into first and second partitions, generating initial Betti curves for the first and second partitions, training a model to recognize Betti curves of the first and second data types on the initial Betti curves, adding each of the first and second test data to each of the first and second partitions to form new first and second partitions, generating new Betti curves for the new first and second partitions, having the model determine whether each of the first and second test data are likely to be first or second data types from new and initial Betti curve deviations and iteratively re-training the model based on determination accuracies.