Disease Prediction Model Using Microbiota Feature Segmentation
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Solution Overview
Problem
Current methods lack effective solutions for disease prediction based on gut microbiota, which is intricate and influences various diseases, such as neurodegenerative disorders and cancer, with imbalances potentially impacting health.
Innovation Solution
A method and system for establishing a disease prediction model that extracts species-level, microbiota interaction, and community-level features from microbiota data, using feature selection models to select relevant features for training a disease prediction model, enhancing the richness and representativeness of feature information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple microbiota features (species-level, interaction, community-level) are extracted and used for disease prediction, then the accuracy of disease prediction is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments microbiota features into three distinct levels: species-level features (individual microbe characteristics), microbiota interaction features (relationships between microbes), and community-level features (overall ecosystem properties). This segmentation allows the complex microbiota system to be analyzed through manageable hierarchical components, improving prediction accuracy while organizing complexity systematically
Solution Approach 2:
The patent introduces multiple dimensional perspectives for analyzing microbiota: taxonomic dimensions (different classification levels), functional dimensions (metabolic capabilities), and interaction dimensions (microbe-microbe and microbe-host relationships). By analyzing data across these multiple dimensions simultaneously, the model achieves higher prediction accuracy without being overwhelmed by single-dimensional complexity
2Loss of information
If comprehensive microbiota features are extracted from multiple levels, then the representativeness of feature information is improved, but the loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary feature extraction and selection before model training by pre-processing microbiota data to extract species-level, interaction, and community-level features. This preliminary action organizes raw microbiota data into structured feature sets in advance, ensuring comprehensive information representation while reducing processing time during actual model training and deployment
Solution Approach 2:
The patent extracts a comprehensive set of features across multiple levels (species, interactions, community) that may initially seem excessive, but then applies feature selection techniques to identify the most informative subset. This approach ensures that no critical information is missed during extraction, while feature selection eliminates redundancy, achieving both high representativeness and efficient processing
Data Source
AI summary
A method for establishing a disease prediction model is provided. The method includes the steps of extracting feature values for multiple microbiota features from microbiota data of each of a plurality of samples, selecting a portion of the extracted microbiota features as selected features, and training a disease prediction model. Each piece of training data used in training the disease prediction model includes (i) disease data for each of the samples and (ii) the feature values of the selected features for the sample. The microbiota features include species-level features, microbiota interaction features, and community-level features.