Gut Microbiome Feature Selection for Enteritis Diagnosis
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Solution Overview
Problem
Existing methods for diagnosing enteritis using machine learning models suffer from performance degradation due to unprocessed samples and large biases among subjects, leading to inaccurate identification of causative agents.
Innovation Solution
A method and apparatus that analyze a mixture of gut-derived substances with a gut environment-like composition, extract microbial data, select microbe-related features using a feature selection algorithm, and train a machine learning model to diagnose enteritis, focusing on genera like Lachnospiraceae, Veillonellaceae, and others.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If bacterial metagenome analysis is performed without sample culturing, then the analysis process is simplified and faster, but the accuracy of deriving causative agents decreases due to large biases among samples
Solution Approach 1:
The patent applies preliminary action by performing sample culturing before metagenome analysis. This preliminary step allows bacteria to grow and multiply, making them detectable and reducing biases among samples. The culturing process prepares the samples in advance, ensuring that subsequent analysis can accurately identify causative agents without the time penalty of waiting for growth during analysis.
2Productivity
If machine learning models are trained using unprocessed samples, then the training process is simpler and faster, but model performance degrades significantly due to noise in training data
Solution Approach 1:
The patent applies preliminary action by processing samples through culturing and selective extraction before feeding them to the machine learning model. This preliminary processing removes noise and enhances relevant microbial signals, ensuring that the training data is of high quality. The feature selection algorithm further refines the data by identifying important microbial features, resulting in better model performance without sacrificing too much training time.
3Loss of information
If all microbial data are used as features in the machine learning model, then more comprehensive information is provided, but the complexity of the model increases and training becomes less efficient
Solution Approach 1:
The patent applies the extraction principle by using a feature selection algorithm to identify and extract only the most relevant microbial features from the comprehensive microbial data. This selective extraction removes redundant and noisy features while retaining the most informative ones related to enteritis diagnosis. The result is a simplified model that maintains high diagnostic accuracy without the complexity of processing all microbial data.
Data Source
AI summary
A method for determining whether enteritis is present by using a machine learning model may include a process of analyzing a mixture of a gut-derived substance collected from a subject and a gut environment-like composition, a process of extracting multiple microbial data based on an analysis result of the mixture, a process of selecting microbe-related features to be used in the machine learning model from the multiple microbial data based on a predetermined feature selection algorithm, a process of training the machine learning model with the microbe-related features, and a process of inputting, to the trained machine learning model, the microbial data collected from the subject to be tested and determining whether enteritis is present. The microbe-related features may include the amount of one or more microbes selected from genera included in families, Ruminococcaceae, Lactobacillaceae, Prevotellaceae, Barnesiellaceae, Bacteroidaceae, Lachnospiraceae, and UCG.010→Lachnospiraceae, Veillonellaceae, Tannerellaceae, Clostridia, Coriobacteriaceae, Butyricicoccaceae, Streptococcaceae, Bacteroidaceae, UCG.010, Prevotellaceae.