Dysbiosis Index Algorithm for Microbiome Health Classification
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Solution Overview
Problem
Current methods fail to consistently characterize host interactions with the gut microbiota to distinguish between health and disease states, particularly in conditions like inflammatory bowel diseases, where precise mechanisms of intestinal microbiota dysfunction remain unclear.
Innovation Solution
A computer-implemented method and system using a machine learning algorithm to assess and classify health status by analyzing the expression levels of non-specific amplicon sequence variants (ASVs) of the V4 region of 16S rRNA in gastrointestinal samples, computing a dysbiosis index score to differentiate between healthy and sick states, and prioritizing donor samples for fecal microbiota transplantation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithm is used to analyze microbiome profile, then health status classification accuracy is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The microbiome profile data is segmented into specific operational taxonomic units (OTUs) with known functional roles. The machine learning model processes these segmented microbial features rather than raw data, improving classification accuracy while managing computational complexity through structured data organization.
Solution Approach 2:
Functional predictions are performed preliminarily on the microbiome profile before the main classification task. This preliminary action transforms raw microbial composition data into functionally meaningful features, enhancing the input quality for the health status classifier and improving overall system accuracy.
2Measurement precision
If comprehensive microbiome analysis is performed to distinguish health from disease states, then diagnostic accuracy is improved, but analysis time and resource requirements increase
Solution Approach 1:
The system extracts and focuses on specific functional predictions and key operational taxonomic units that are most relevant to health status differentiation. By extracting only the critical microbial features rather than analyzing the entire microbiome profile in detail, the system maintains high diagnostic accuracy while reducing analysis time and computational resources required.
3Adaptability or versatility
If machine learning model is trained on multiple disease cohorts, then generalizability across diseases is improved, but training data requirements and model complexity increase
Solution Approach 1:
The machine learning model is designed with universal functionality to handle multiple disease cohorts and health conditions. By training on diverse datasets encompassing various diseases and using standardized functional prediction frameworks, the model learns general patterns of microbiome-disease relationships that apply across different conditions, improving generalizability without requiring separate models for each disease.
Data Source
AI summary
Herein disclosed are a computer-implemented method, a system, and a kit for assessing/classifying the health status associated with a microbiome profile of a gastrointestinal (GI) sample. The method includes receiving data regarding expression levels of amplicon sequence variants (ASVs) of a V4 region of 16S rRNA in a GI sample of a subject, and utilizing a machine learning algorithm that is trained to distinguish a healthy state from a sick state, a score is computed, and a prediction is made for the presence of a general microbial response that is shared by a large variety of diseases.