Machine Learning applied to MALDI-TOF data in a clinical setting: a systematic review
Uloženo v:
| Vydáno v: | bioRxiv (Mar 3, 2025) |
|---|---|
| Hlavní autor: | |
| Další autoři: | , , , , |
| Vydáno: |
Cold Spring Harbor Laboratory Press
|
| Témata: | |
| On-line přístup: | Citation/Abstract Full text outside of ProQuest |
| Tagy: |
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3160657507 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2692-8205 | ||
| 024 | 7 | |a 10.1101/2025.01.25.634879 |2 doi | |
| 035 | |a 3160657507 | ||
| 045 | 0 | |b d20250303 | |
| 100 | 1 | |a Schmidt-Santiago, Lucia | |
| 245 | 1 | |a Machine Learning applied to MALDI-TOF data in a clinical setting: a systematic review | |
| 260 | |b Cold Spring Harbor Laboratory Press |c Mar 3, 2025 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Bacterial identification, antimicrobial resistance prediction, and strain typification are critical tasks in clinical microbiology, essential for guiding patient treatment and controlling the spread of infectious diseases. While Machine Learning (ML) has shown immense promise in enhancing MALDI-TOF mass spectrometry applications for these tasks, an up to date comprehensive review from a ML perspective is currently lacking. To address this gap, we systematically reviewed 93 studies published between 2004 and 2024, focusing on key ML aspects such as data size and balance, pre-processing pipelines, model selection and evaluation, open-source data and code availability. Our analysis highlights the predominant use of classical ML models like Random Forest and Support Vector Machines, alongside emerging interest in Deep Learning approaches for handling complex, high-dimensional data. Despite significant progress, challenges such as inconsistent preprocessing workflows, reliance on black-box models, limited external validation, and insufficient open-source resources persist, hindering transparency, reproducibility, and broader adoption. This review offers actionable insights to enhance ML-driven bacteria diagnostics, advocating for standardized methodologies, greater transparency, and improved data accessibility. In addition, we provide guidelines on how to approach ML for MALDI-TOF analysis, helping researchers navigate key decisions in model development and evaluation.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Final version submitted to journal. Text, figures and tables are updated and have been revised. | |
| 653 | |a Antimicrobial resistance | ||
| 653 | |a Machine learning | ||
| 653 | |a Mass spectrometry | ||
| 653 | |a Types | ||
| 653 | |a Mass spectroscopy | ||
| 653 | |a Scientific imaging | ||
| 653 | |a Clinical microbiology | ||
| 653 | |a Deep learning | ||
| 653 | |a Classification | ||
| 653 | |a Infectious diseases | ||
| 653 | |a Learning algorithms | ||
| 700 | 1 | |a Guerrero-Lopez, Alejandro | |
| 700 | 1 | |a Sevilla-Salcedo, Carlos | |
| 700 | 1 | |a Rodriguez-Temporal, David | |
| 700 | 1 | |a Rodriguez-Sanchez, Belen | |
| 700 | 1 | |a Gomez-Verdejo, Vanessa | |
| 773 | 0 | |t bioRxiv |g (Mar 3, 2025) | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3160657507/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://www.biorxiv.org/content/10.1101/2025.01.25.634879v2 |