Microbial Consortium Discovery Using ML for Disease-Specific Probiotics
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Solution Overview
Problem
Current probiotics face challenges in achieving high viable cell numbers and maintaining long-term viability, and existing methods for determining diseases using microbial information are limited in their ability to identify disease-specific microbial consortia effectively.
Innovation Solution
A method using a machine learning model to derive microbial cluster data by collecting gut microbiota data, drawing candidate microbial clusters, and identifying disease-relevant microbial consortia through supervised and unsupervised learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single bacterial strain is used as probiotics, then the treatment is simpler and easier to manufacture, but the improvement effect is insufficient and long-term viability is difficult to maintain
Solution Approach 1:
The patent combines multiple bacterial strains into a consortium to achieve synergistic effects that improve disease treatment efficacy. The machine learning model identifies combinations of bacteria that work together more effectively than individual strains, resolving the contradiction between manufacturing simplicity and treatment effectiveness.
Solution Approach 2:
The patent creates a composite probiotic product consisting of multiple bacterial strains selected and combined based on machine learning analysis. This composite consortium provides enhanced stability and efficacy compared to single-strain probiotics, while the automated ML-based selection process maintains manufacturing efficiency.
2Reliability
If the entire flora of a healthy person is transplanted (FMT), then the gut microbiota structure is completely restored, but the treatment is complex and carries higher risks
Solution Approach 1:
The patent extracts and selects only the specific beneficial bacterial strains from healthy gut microbiota that are relevant to treating particular diseases. The machine learning model identifies and isolates key strains from the complex full microbiota, creating a simplified yet effective probiotic consortium that maintains treatment efficacy while reducing complexity and risk.
Solution Approach 2:
The patent segments the complex gut microbiota into disease-specific functional modules or consortia. Rather than transplanting the entire microbiota, the ML model divides and selects only the necessary bacterial components for treating specific conditions, reducing treatment complexity while preserving efficacy.
3Device complexity
If traditional statistical methods are used to analyze microbial information, then the analysis process is simpler, but the ability to identify disease-specific microbial consortia is limited
Solution Approach 1:
The patent replaces traditional statistical analysis methods with machine learning algorithms that can process complex microbial data more effectively. The ML model automatically identifies patterns and relationships in microbiota data that traditional statistics miss, significantly improving disease-specific consortium identification accuracy while the automated nature maintains computational efficiency.
Data Source
AI summary
The present invention aims to discover a microbial cluster that can be used when developing effective probiotics for a specific disease. Specifically, the invention includes (1) discovering a candidate microbial cluster data related to specific disease, which is collected by preprocessing a gut microbiota data (a taxonomy abundance table) and (2) training the machine learning model using the collected candidate microbial cluster data, and selecting a model with the highest predictive performance to discover a disease-microbial consortium.