Oral Microbiome Functional Profiling for Dental Caries Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting dental caries are limited by the complexity of oral microbiome data, leading to statistical artifacts and difficulty in interpreting microbiome interactions, resulting in inaccurate diagnosis and treatment plans.
Innovation Solution
A system and method using metatranscriptomics and metagenomics analysis, combined with machine learning, to identify taxon clusters, generate phylogenomic functional categories, and determine module completion ratios, enabling accurate prediction of dental caries through feature selection algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If metatranscriptomics and metagenomics analysis are used to predict dental caries, then prediction accuracy is improved (>99% accuracy), but data complexity and computational requirements increase
Solution Approach 1:
The system segments the complex microbiome data into manageable functional categories (PGFCs) and taxon clusters (TCs), processing data at multiple hierarchical levels from individual genes to community-level patterns, enabling accurate prediction while managing computational complexity through structured data organization
Solution Approach 2:
The patent introduces intermediate representation layers including PGFCs as mediators between raw sequencing data and final caries predictions, and employs machine learning models as intermediary processing layers that transform complex microbiome profiles into actionable diagnostic predictions
2Reliability
If association networks are used to analyze microbiome data, then biological interactions are identified, but statistical artifacts and spurious associations increase due to compositional data issues
Solution Approach 1:
The system changes the mathematical parameters used for analyzing microbiome data by implementing compositionally-aware metrics that account for the relative nature of sequencing data, transforming traditional correlation analyses into proportionality-based approaches that eliminate spurious associations while preserving genuine biological interactions
3Productivity
If traditional correlation metrics are used for microbiome analysis, then computational processing is simplified, but biologically meaningful associations are lost due to compositional data fallacies
Solution Approach 1:
The patent changes the analytical parameters from traditional Euclidean distances and correlations to compositionally-aware metrics such as Aitchison distance and proportionality coefficients, enabling both computationally efficient processing and biologically accurate association detection by respecting the relative nature of microbiome sequencing data
4Ease of operation
If dimensionality reduction methods like PCA are applied to microbiome data, then data interpretation is simplified, but accessibility to original biological features is lost
Solution Approach 1:
The system segments the dimensionality reduction process into interpretable components by organizing data into taxon clusters (TCs) and phylogenomic functional categories (PGFCs), maintaining a hierarchical structure that preserves links to original biological features while providing simplified aggregated views for easier interpretation
Data Source
AI summary
A sequencing module configured to provide metatranscriptomic reads from an oral sample from a subject; metatranscriptomic reads from the sequencing of oral sample and identify and cluster microbes identified in the oral sample into taxon clusters (TCs) using the metatranscriptomic reads mapped to a metagenomic library; generate TC-specific orthogroups for each of the TCs via protein clustering; determine KEGG orthology for each of the TC-specific orthogroups, or genes directly; generate phylogenomic functional categories (PGFCs) from grouping of gene expression counts by the KEGG modules for each of the TCs; retain the PGFCs having an MCR above an MCR threshold to obtain input data; and identify or predict, using a classifier model including variables selected by a feature selection machine learning algorithm, dental caries in said subject based on the input data.