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MALDI-TOF Mass Spectrometry

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Conformal Prediction with Knowledge Graphs for Reliable Antimicrobial Resistance Detection with MALDI-TOF Mass

Nina Corvelo Benz^1,2, Lucas Miranda³, Dexiong Chen³

¹Max Planck Institute for Software Systems, Kaiserslautern, Germany.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|December 5, 2025

Summary

This study introduces a new framework for faster bacterial antimicrobial resistance detection using knowledge-graph-enhanced conformal prediction. The method provides reliable predictions with uncertainty estimates, improving early treatment decisions.

Keywords:

MALDI-TOF MS antimicrobial resistance conformal prediction medical data science uncertainty estimation

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Area of Science:

Microbiology and Infectious Diseases
Computational Biology and Bioinformatics
Clinical Diagnostics

Background:

Bacterial antimicrobial resistance (AMR) is a critical global health threat, necessitating rapid diagnostics.
Current culture-based methods for AMR detection are slow (≥48 hours).
Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) offers faster species identification but requires enhanced AMR prediction capabilities.

Purpose of the Study:

To develop a novel framework for rapid and reliable antimicrobial resistance prediction.
To improve the clinical integration of MALDI-TOF diagnostics for AMR detection.
To provide statistically grounded uncertainty estimates for resistance predictions.

Main Methods:

Proposed a knowledge-graph-enhanced conformal predictor for AMR detection.
Integrated domain knowledge via a drug- and species-specific knowledge graph.
Developed a novel classifier surpassing state-of-the-art models.
Evaluated the framework on *Klebsiella pneumoniae* and *Escherichia coli* using the DRIAMS dataset.

Main Results:

The conformal predictor achieved expected coverage guarantees.
Knowledge-graph enhancement significantly reduced false discovery rates compared to standard conformal prediction.
The framework demonstrated improved predictive performance and provided uncertainty estimates.

Conclusions:

The developed framework enhances the reliability of early AMR predictions from MALDI-TOF data.
Statistically grounded uncertainty estimates and improved performance support clinical integration of rapid diagnostics.
This approach enables better-informed early treatment decisions, combating AMR effectively.