Mass Spectrometry Score Normalization for Microorganism Identification
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Solution Overview
Problem
Current mass spectrometry-based microorganism identification techniques face challenges in reliably distinguishing between microorganisms due to ambiguous distance values generated by classification tools, making it difficult to accurately identify unknown microorganisms among hundreds of possibilities.
Innovation Solution
An algorithm that transforms distance values into normalized probabilities using Gaussian random variables, allowing for a more reliable identification of microorganisms by calculating the likelihood of an unknown microorganism belonging to a specific reference microorganism based on the distance from its boundary in a vectorial space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classification tools calculate algebraic distance between unknown microorganism and reference boundaries, then identification capability is provided, but the distance values are ambiguous and difficult to interpret for reliable identification
Solution Approach 1:
The patent transforms the algebraic distance parameter into a probability score through a monotonic transformation function. This changes the parameter representation from ambiguous distance values to interpretable probability values between 0 and 1, where higher values indicate higher confidence in identification. The transformation preserves the ordering information while making the results more meaningful for users.
Solution Approach 2:
The patent introduces a monotonic transformation function as an intermediary between the classification tool's distance calculation and the final identification result. This intermediary function converts the mathematically useful but interpretably difficult distance values into probability scores that are both mathematically sound and easily interpretable by users.
2Adaptability or versatility
If hundreds of reference microorganisms are compared using multiple peaks, then identification coverage is improved, but the complexity of analyzing and comparing distance values increases substantially
Solution Approach 1:
The patent applies parameter transformation to convert multiple ambiguous distance values into a single set of probability scores. This transformation simplifies the interpretation process even when comparing hundreds of reference microorganisms, as users only need to evaluate the transformed probability values rather than analyzing complex distance relationships across multiple peaks and references.
3Productivity
If direct distance analysis is used to identify microorganisms, then the method is simple and fast, but accuracy is reduced in ambiguous cases
Solution Approach 1:
The patent applies a monotonic transformation function to the distance values before final interpretation. This preliminary transformation step prepares the data by converting it into a more informative format without requiring additional computational resources or time, thus maintaining speed while improving accuracy.
Solution Approach 2:
The patent transforms the distance parameter into a probability score using a monotonic function. This parameter change enhances the discriminative power of the identification process, allowing for more accurate distinction between similar microorganisms while maintaining the computational efficiency of the original distance-based approach.
Data Source
AI summary
An identification by mass spectrometry of a microorganism from among reference microorganisms represented by reference data sets includes: determining a set of data of the microorganism according to a spectrum; for each reference microorganism, calculating a distance between the determined and reference sets; and calculating a probability ƒ(m) according to relationf(m)=pN(m|μ,σ)pN(m|μ,σ)+(1-p)N(m|μ_,σ_)where: m is the distance calculated for the reference microorganism; N(m|μ,σ) is the value, for m, of a random variable modeling the distance between a reference microorganism to be identified and the reference microorganism, when the microorganism is the reference microorganism; N(m|μ,σ) is the value, for m, of a random variable modeling the distance between a microorganism to be identified and the reference microorganism, when the microorganism is not the reference microorganism; and p is a scalar in the range from 0 to 1.