ArticleRapid communications in mass spectrometry : RCM2025
Part B: SLICE-MSI-A Machine Learning Interface for System Suitability Testing of Mass Spectrometry Imaging Platforms.
Article in Rapid communications in mass spectrometry : RCM, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Part A: Implementing an Analyte Panel and Sampling Protocol for Quality Control in Mass Spectrometry Imaging.Rapid communications in mass spectrometry : RCM · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
rationaleThe field of mass spectrometry imaging is currently devoid of standardized protocols or commercially available products designed for system suitability testing of MSI platforms. Machine learning is an approach that can quickly and effectively identify complex patterns in data and use them to make informed classifications, but there is a technical barrier to implementing these algorithms. Here we package the machine learning algorithms into a user-friendly interface to make community-wide implementation of this protocol possible.
methodsThe software package is built entirely in the Python language using the PySimpleGUI library for the construction of the interface, Pandas and Numpy libraries for data formatting and manipulation, and the Scikit-Learn library for the implementation of machine learning algorithms. Training data is collected on an instrument under clean and compromised conditions that can then be used to evaluate model performance and to train models prior to interrogating unknown samples before, during, or after experiments.
resultsDetailed instructions are provided for the effective use of the SLICE-MSI software package to use machine learning to evaluate instrument condition of MSI platforms. File formatting and generalizable steps are clearly described to make the implementation of this package easy for multiple labs and different MSI platform configurations.
conclusionsIn this protocol, we demonstrate SLICE-MSI, a machine learning graphical user interface for efficient and easy implementation of QC instrument classification of mass spectrometry imaging platforms.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.