Mass Spectrometry Data Support Using ML Batch Segmentation
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Solution Overview
Problem
Conventional mass spectrometry techniques face challenges in real-time or near-real-time data analysis due to the computational intensity required for processing large batches of data, such as thousands or tens of thousands of raw spectrum files, which makes it infeasible for immediate processing.
Innovation Solution
A scientific instrument support apparatus and method that utilizes a machine learning model to process raw spectrum files by dividing them into subsets, generating spectrum match files, and applying preprocessing techniques like peak detection, noise removal, and mass calibration, allowing for efficient data analysis and generation of results lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques process the entire batch of raw spectrum files multiple times to generate screening lists and results, then comprehensive data analysis is achieved, but computational burden increases and real-time processing becomes infeasible
Solution Approach 1:
The patent divides the batch of raw spectrum files into multiple subsets and processes them separately through multiple passes. In the first pass, a first subset is processed to generate initial screening lists. In subsequent passes, additional subsets are processed with updated screening lists. This segmentation allows comprehensive analysis while reducing the computational burden of processing the entire batch simultaneously, enabling real-time or near-real-time processing.
2Reliability
If the entire batch of raw spectrum files is processed multiple times, then accurate screening lists and results are generated, but processing time increases
Solution Approach 1:
The patent performs preliminary processing of a first subset of raw spectrum files to generate initial screening lists before processing the remaining subsets. These preliminary screening lists are then used as inputs for subsequent processing passes. This preliminary action reduces the overall processing time while maintaining accuracy, as the screening lists are progressively refined without requiring multiple complete batch processing cycles.
Solution Approach 2:
The patent implements a multi-pass processing approach where different subsets of raw spectrum files are processed in periodic passes. The first pass processes a first subset to generate initial screening lists, and subsequent passes process additional subsets with updated screening lists. This periodic action allows the system to balance accuracy and processing time by iteratively refining results without repeatedly processing the entire batch.
3Measurement precision
If machine learning models process each raw spectrum file individually, then accurate spectrum match files are generated, but computational resources are heavily consumed
Solution Approach 1:
The patent segments the batch of raw spectrum files into multiple subsets and processes them separately through machine learning models. Each subset is processed to generate corresponding spectrum match files, which are then used to update screening lists. This segmentation reduces the computational resource consumption at any given time while maintaining spectrum matching accuracy through iterative processing of all subsets across multiple passes.
Data Source
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AI summary
Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a scientific instrument support apparatus including memory hardware configured to store instructions and processing hardware configured to execute the instructions. The instructions include loading a batch of raw spectrum files generated by a mass spectrometer, dividing the raw spectrum files into a first subset and a second subset, processing each of the first subset of raw spectrum files with a machine learning model to generate a first subset of spectrum match files, generating a screening list from the first subset of spectrum match files, and processing each of the second subset of raw spectrum files and the screening list with the machine learning model to generate a second subset of spectrum match files.