Microbiome Functional Classifier Using Sequence Matrix Clustering
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Solution Overview
Problem
The complexity of the human microbiome, with numerous unknown features exceeding known observations, makes it difficult for supervised learning AI systems to be properly trained for microbiome classification, hindering the understanding of microbiome imbalances and their association with physiological conditions and diseases.
Innovation Solution
A method involving the construction of a microbiome classifier using a reference database of biological sequences annotated with a coding system, computing pair-wise distance, clustering, and constructing a taxonomic tree to classify microbiomes, which allows for the classification of microbiomes using any coding system, such as InterProScan, KEGG/EC, or Gene Ontology, by comparing individual domain sequences with unique identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning AI systems are used to classify microbiome data, then classification capability is provided, but the large number of unknown features exceeds the small number of known observations making proper training difficult
Solution Approach 1:
The patent introduces functional profiles as an intermediary layer between raw microbiome sequences and classification. Instead of directly training on high-dimensional sequence data with many unknown features, the system first processes sequences through functional profiles that aggregate information into lower-dimensional functional categories. This intermediary representation reduces the feature space while preserving biologically relevant information, enabling supervised learning to work effectively despite the original data sparsity.
2Quantity of substance
If the number of taxa in collected samples is large, then microbiome diversity is captured, but statistical analysis becomes difficult
Solution Approach 1:
The patent segments the complex microbiome data into functional profiles that group taxa by their functional characteristics rather than treating each taxon independently. This segmentation transforms the analysis from dealing with individual high-diversity taxa to analyzing aggregated functional categories, reducing statistical complexity while maintaining the ability to capture microbiome diversity through functional variation.
Data Source
AI summary
A method for classifying microbial function within any microbiome can be carried out with any coding system. The method, which does not entail measuring the distance between sequences, includes: (1) selecting a reference database that links a coding system to a set of biological sequences; (2) constructing an N×M matrix with each row (N) representing a code from the coding system, each column (M) representing a single biological sequence from the set, and cells representing the presence, absence, or frequency of the single biological sequence for one or more codes; (3) computing the pair-wise distance between the rows of the matrix to form an N×N matrix, wherein N is the number of codes in the matrix; (4) clustering the results to form a data tree; (5) generating a taxonomic tree from the cluster results; and (6) applying a classification tool to the taxonomic tree to classify the microbiome.