Negative Marker Machine Learning for Microorganism Identification
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Solution Overview
Problem
Conventional methods face challenges in accurately identifying and distinguishing similar microorganism species due to similar mass spectral patterns, which affects the accuracy of microorganism identification and classification, particularly in cases like mycobacteria where different species require different treatments.
Innovation Solution
The use of negative markers in conjunction with machine learning schemes, such as SVM, k-NN, and random forest algorithms, to improve microorganism identification and classification by extracting and preprocessing mass information using TF-IDF calculations and bin-based features, enhancing the classification performance regardless of the machine learning scheme applied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mass spectrometry methods are used to identify microorganisms, then the identification process is simple and fast, but the identification accuracy for similar species is low
Solution Approach 1:
The patent segments the mass spectral data into multiple bin sets with different resolution levels. Coarse bin sets capture overall spectral patterns while fine bin sets resolve subtle differences between similar species. This multi-resolution segmentation enables accurate discrimination of similar microorganisms without requiring overly complex analysis methods.
Solution Approach 2:
The patent transforms the one-dimensional mass spectral data into multi-dimensional feature space by creating multiple bin sets that represent different aspects of the spectral information. This dimensional transformation allows machine learning algorithms to better distinguish between similar species by considering multiple features simultaneously rather than relying on single spectral patterns.
2Measurement precision
If multiple mass spectral parameters are used to distinguish similar species, then the identification accuracy improves, but the complexity of analysis increases
Solution Approach 1:
The patent performs preliminary processing of mass spectral data by pre-defining multiple bin sets and pre-calculating relevant features before classification. This preliminary action organizes the complex spectral data into structured formats that are optimized for machine learning analysis, reducing the computational complexity during the actual identification process while maintaining high discrimination accuracy.
Solution Approach 2:
The patent introduces machine learning algorithms as intermediaries between the complex mass spectral data and the final classification decision. These algorithms automatically learn the optimal combination of multiple spectral parameters from training data, eliminating the need for manual feature selection and reducing analysis complexity while improving accuracy for distinguishing similar species.
3Measurement precision
If conventional machine learning schemes are applied without negative markers, then the classification process is straightforward, but the identification performance for similar species is insufficient
Solution Approach 1:
The patent applies negative markers that identify characteristics absent in target species but present in similar species. This inverted approach complements traditional positive markers by highlighting what distinguishes similar species from the target, providing contrasting information that significantly improves classification accuracy when combined with conventional machine learning schemes.
Solution Approach 2:
The patent creates a composite feature set that combines multiple types of markers including positive markers, negative markers, and multi-resolution bin set features. This composite approach integrates diverse information sources into a unified feature representation that enhances the ability of machine learning algorithms to accurately classify similar microorganism species.
Data Source
AI summary
The present disclosure relates to a method and apparatus for identification of similar species, and more particularly to method and apparatus for identification of similar species based on machine learning using negative markers. According to an aspect of the present disclosure, a method for identifying similar species may comprise: extracting first mass information for an input sample; classifying the input sample using a machine learning model based on at least a negative marker, based on the first mass information; and identifying a species for the input sample based on the classification result.