Microbiome Aging Clock Using Deep Neural Networks
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Solution Overview
Problem
Current methods for measuring biological aging face challenges in developing objective, quantifiable biomarkers that accurately reflect the complex physiological changes across multiple systems, particularly due to high individual variation in gut microbiota, making it difficult to establish a reliable biological aging clock.
Innovation Solution
A method involving isolating microorganism nucleic acids from a sample, generating a taxonomic profile, and processing it with a machine learning platform, specifically using deep neural networks, to predict chronological or phenotypical age based on gut microbiota composition, allowing for personalized biological aging assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gut microbiota composition is used as a biomarker for biological aging, then personalized biological aging assessment can be achieved, but high individual variation in microflora makes it difficult to establish a reliable aging clock
Solution Approach 1:
The patent transforms the microbiota data from raw composition to functional potential profiles by analyzing metagenomic sequences and comparing them against reference databases of microbial genes and pathways. This parameter transformation from taxonomic composition to functional capacity reveals more consistent aging-related patterns despite individual variation in microbial species presence.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between raw microbiota data and biological aging assessment. These models are trained on large datasets to identify complex patterns and relationships that are not apparent through traditional analysis, thereby improving reliability by filtering out noise from individual variation while preserving meaningful aging signals.
2Measurement precision
If complex machine learning models are used to analyze microbiota data, then prediction accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent divides the complex analysis into sequential modules: (1) metagenomic sequence processing and assembly, (2) functional gene annotation and pathway reconstruction, (3) feature extraction from functional profiles, and (4) machine learning prediction. This segmentation allows each module to be optimized independently and facilitates parallel processing to reduce overall computational burden.
Solution Approach 2:
The patent performs preliminary processing of microbiota data by pre-computing functional potential profiles and extracting relevant features before feeding them to the machine learning model. This preliminary action reduces the dimensionality and complexity of the input data, making the subsequent prediction step more computationally efficient while maintaining accuracy.
Data Source
AI summary
A method of predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject can include: isolating a plurality of microorganism nucleic acids of microorganisms from a sample of a microbiota of the subject; analyzing the plurality of microorganism nucleic acids to determine an amount of the microorganisms of the microbiota based on the plurality of microorganism nucleic acids; generating a taxonomic profile of the microbiota of the subject based on the amount of each of the microorganisms; processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject; generating a report with the predicted phenotypical age of the subject; and providing the report to the subject.