Molecular Network Generation Using Spectral Library Intermediaries
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Solution Overview
Problem
Existing molecular networking frameworks face challenges in accurately and efficiently generating and visualizing molecular networks due to the use of multiple unknown data points, leading to compounded errors and time-consuming, difficult interpretation of results, especially in untargeted metabolomics datasets.
Innovation Solution
A novel system that employs limited unknown data points to generate molecular networks, using known spectral data to classify and identify unknown spectra, with a dynamically adjustable and customizable molecular network visualization, allowing for rapid and accurate spectral data grouping and chemical relationship analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple unknown data points are used in molecular networking, then the network can be generated from untargeted datasets, but compounded errors occur and interpretation becomes time-consuming and difficult
Solution Approach 1:
The patent introduces a spectral library of known compounds as an intermediary between unknown spectra and molecular network generation. Unknown spectra are compared against this library to identify known compounds, which then serve as reliable anchors for network construction. This mediator approach allows processing of untargeted datasets while maintaining accuracy by reducing reliance on multiple unknown data points.
2Adaptability or versatility
If multiple unknown data points are used in molecular networking, then the network can be generated from untargeted datasets, but the process becomes time-consuming and difficult to interpret
Solution Approach 1:
By using a spectral library of known compounds as an intermediary, the system automatically identifies unknown spectra through comparison, eliminating time-consuming manual interpretation. The known compounds serve as reference points that automatically structure the molecular network, making the process both faster and more interpretable while maintaining the ability to handle untargeted datasets.
3Reliability
If limited unknown data points are used, then errors are reduced and identification speed improves, but the ability to process complex untargeted datasets is challenged
Solution Approach 1:
The patent segments the spectral data processing into two distinct phases: first, comparison against a spectral library of known compounds to identify reliable matches with limited unknown data points; second, use of these identified compounds as anchors to progressively build and expand the molecular network. This segmentation allows accurate grouping while gradually incorporating complexity of untargeted datasets.
Solution Approach 2:
The system performs preliminary identification of known compounds by comparing unknown spectra against a pre-built spectral library before constructing the molecular network. This preliminary action establishes reliable reference points that enable subsequent network expansion to handle complex untargeted datasets while maintaining accuracy through the constraint of using limited verified unknown data points.
Data Source
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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.