Machine learning facilitates the application of mass spectrometry-based metabolomics to clinical analysis: A review of early diagnosis of high mortality rate cancers

Hiu Lok Ngan, Ka Yam Lam, Zhichao Li, Jialing Zhang, Zongwei Cai*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)

Abstract

Early cancer detection is critical to control disease progression. Research of high mortality rate cancers is thereby essential although cancer diagnosis is challenging. Mass spectrometry (MS)-based metabolomics is a great technique to support. Despite MS-based metabolomics being a high-throughput technique, it can be inefficient due to the exponentially increased data volume and complexity of MS data. To demonstrate the real power of the MS-based platform, machine learning (ML) can provide insights from the sea of data. As an emerging technology, it is worth reviewing the research work on how ML coupled with MS-based metabolomics in the past decade. An overview of clinical metabolomics studies is presented with respect to cancer type. The primary purpose of this review is to present what data strategy has been applied previously and discuss the challenges and potential solutions when MS-based metabolomics and ML are combined for early cancer diagnosis.
Original languageEnglish
Article number117333
JournalTrAC - Trends in Analytical Chemistry
Volume168
DOIs
Publication statusPublished - Nov 2023

Scopus Subject Areas

  • Analytical Chemistry
  • Spectroscopy
  • Computer Science Applications

User-Defined Keywords

  • Metabolomics
  • Mass spectrometry
  • Machine learning
  • High mortality rate cancers
  • Early cancer diagnosis
  • Feature selection

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