Mass Spectrometry Signal Retrieval Using ML Retention Windows
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Solution Overview
Problem
Conventional mass spectrometry data extraction techniques face inefficiencies and unreliability, particularly in handling large volumes of complex biological samples, due to minor errors in mass-to-charge ratios, noise prevalence, and failure to detect actual signals, leading to inconsistent results and manual processing infeasibility.
Innovation Solution
An image-based processing approach combined with machine learning models is employed to process and transform raw mass spectrometry data, mitigating noise and enhancing signal detection, enabling accurate analysis of thousands of samples efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional mass spectrometry data extraction techniques are used, then processing of complex biological samples can be performed, but the results are inconsistent and unreliable due to noise and detection failures
Solution Approach 1:
The patent segments the mass spectrometry data processing into multiple stages: raw data acquisition, noise filtering, signal enhancement, and analysis. By dividing the complex processing task into manageable segments, the system improves reliability by applying specialized techniques at each stage rather than using a single conventional method throughout.
Solution Approach 2:
The patent introduces intermediate processing steps between data acquisition and final analysis, including noise filtering algorithms and signal enhancement techniques. These intermediary processes act as mediators that clean and prepare the data, improving both reliability and measurement precision by removing noise before analysis.
2Reliability
If manual processing methods are used to handle detection errors, then some inconsistencies can be corrected, but processing time becomes prohibitively long and scalability is lost
Solution Approach 1:
The patent implements automated quality control and error correction mechanisms that operate without manual intervention. The system self-corrects detection errors through algorithmic processes, maintaining data consistency while processing thousands of samples automatically, thus preserving both reliability and productivity.
Solution Approach 2:
The patent incorporates feedback loops where processing results are continuously monitored and used to adjust processing parameters. This automated feedback mechanism maintains data consistency across large sample volumes without requiring manual review, enabling both high reliability and fast processing speeds.
3Measurement precision
If traditional data processing approaches are used, then standard analysis can be performed, but resource consumption is high and processing efficiency is low
Solution Approach 1:
The patent performs preliminary noise filtering and data preprocessing before main analysis to reduce the computational burden. By preparing the data in advance and removing irrelevant information, the system maintains analysis accuracy while significantly reducing computational resource consumption during the main processing stage.
Solution Approach 2:
The patent extracts and removes noise and irrelevant data components before performing detailed analysis. By separating the useful signal from noise early in the process, the system reduces the amount of data that requires intensive computational processing, thereby lowering resource consumption while preserving measurement precision.
Data Source
AI summary
Systems and methods are provided for obtaining raw mass spectrometry data from samples, generating an image representation from the raw mass spectrometry data, selecting a portion of the signals corresponding to the image representation, inputting the selected portion into a machine learning model to determine or infer an existence or an absence of signals within respective retention time windows, obtaining a retention time window within which a subset of the signals exist, determining whether to expand the retention time window, determining or receiving an indication of a retention time window within which a subset of the signals are located, and determining whether to expand the retention time window. The systems and methods may selectively expand the retention time window based on the determination, and retrieve information within the expanded retention time window or the retention time window. The image representation indicates intensities of signals from the samples