# LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening

## TL;DR

LCMS-Net is a deep learning model that automates and improves the analysis of mass spectrometry data for forensic and medical applications.

## Contribution

LCMS-Net introduces a simpler, faster, and more efficient deep learning approach for analyzing raw LC-HRMS data without manual preprocessing.

## Key findings

- LCMS-Net achieved a 9% F1-score improvement for cause-of-death screening compared to OPLS-DA.
- LCMS-Net improved colon cancer detection by 1.8% F1-score over DeepMSProfiler.
- LCMS-Net reduces batch effects across different instruments with less than 3% performance variation.

## Abstract

Current preprocessing workflows for untargeted metabolomics
using
liquid chromatography-high resolution mass spectrometry (LC-HRMS)
are time-consuming and require significant domain knowledge. Furthermore,
they lack reproducibility or may fail to detect some metabolites entirely.
We introduce LCMS-Net, an end-to-end deep learning model for the analysis
of LC-HRMS data, to address these challenges. LCMS-Net mitigates the
need for manual data preprocessing by operating directly on the raw
LC-HRMS data and explicitly modeling its spatial properties. The effectiveness
of this fully automated workflow is shown through two case-studies,
cause-of-death (CoD) screening and colon cancer detection. For the
cause-of-death screening task, LCMS-Net achieved a 9% improvement
in F1-score compared to the previous state-of-the-art model (OPLS-DA).
For the colon cancer detection task, LCMS-Net achieved an F1-score
improvement of 1.8% compared to the previous state-of-the-art model
(DeepMSProfiler). Furthermore, LCMS-Net significantly reduces batch
effects that are a common source of bias in metabolomics data analyses.
This was shown by using a training and test set from different measurement
instruments, where the performance only differed by at most 3% as
to using data from the same instrument. Compared to other end-to-end
deep learning methods for LC-HRMS data, LCMS-Net is also structurally
simpler and does not rely on pretraining, which makes it faster and
computationally more efficient.