Molecular Spectral Networking Using Library-Mediated Unknown Classification
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Solution Overview
Problem
Existing molecular networking frameworks face challenges in accurately and efficiently annotating and identifying metabolites in untargeted metabolomics datasets due to the use of plural unknown data points, leading to compounded errors and time-consuming, difficult interpretation of long lists of hits.
Innovation Solution
A novel system that employs limited unknown data points to generate a molecular network, using known spectral data to classify and identify unknown spectra, with dynamic visualization and customization options, reducing errors and improving annotation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing molecular networking frameworks use plural unknown data points for metabolite identification, then the coverage of unknown metabolites increases, but the error rate compounds and interpretation becomes time-consuming
Solution Approach 1:
The patent introduces a spectral library of known compounds as an intermediary between unknown metabolites and identification results. Instead of directly comparing unknown data points with each other (which compounds errors), the system mediates through reference spectra of identified compounds, enabling accurate classification without compounding errors
Solution Approach 2:
The system performs preliminary classification by comparing unknown spectra against a pre-built library of known spectral patterns before final identification. This preliminary action filters and organizes unknown data points using established reference data, reducing the complexity and error propagation in subsequent identification steps
2Quantity of substance
If existing frameworks analyze long lists of hits for unknown metabolites, then comprehensive identification is achieved, but the process becomes time-consuming and difficult to interpret
Solution Approach 1:
The patent extracts and prioritizes the most relevant spectral matches from the complete set of possible hits by comparing against a reference library. Instead of presenting all possible matches, the system extracts only the most significant and relevant identifications, reducing interpretation time while maintaining comprehensiveness
Solution Approach 2:
The system changes the parameter of comparison by using spectral similarity metrics and scoring systems that rank matches by relevance. This parameter transformation converts an unmanageable list of equal-weight hits into a prioritized ranking, enabling rapid identification of the most likely matches without losing comprehensive coverage
Data Source
AI summary
Embodiments described herein relate to a process for molecular network generation. A system can comprise a memory that stores, and a processor that executes, computer executable components. The computer executable components can comprise an evaluating component that executes a comparison of first spectrum data to second spectrum data, a scoring component that, based on the comparison, generates a spectrum similarity score describing a level of similarity of the first spectrum data to the second spectrum data, a parameterizing component that, based on the comparison, associates a first secondary property corresponding to the first spectrum data with the second spectrum data or associates a second secondary property corresponding to the second spectrum data with the first spectrum data, and a generating component that generates a grouping of spectral data comprising the first spectrum data and the second spectrum data based on the spectrum similarity score and on the associating.