ReviewFrontiers in molecular biosciences2024
Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases.
Review in Frontiers in molecular biosciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Molecular Age Estimation: Current Perspectives and Future Considerations.International journal of molecular sciences · 2026Review
- Integrating Robotic Bilateral Axillo-Breast Approach Thyroidectomy with Molecular Diagnostics and Artificial Intelligence in Thyroid Cancer Care.Biomolecules & therapeutics · 2026Review
- Integrative mass spectrometry-driven multi-omics and single cell technologies in ankylosing spondylitis: insights into pathogenesis, biomarker discovery, and precision medicine.Journal of translational autoimmunity · 2025Review
- Robustness of steroidomics-based machine learning for diagnosis of primary aldosteronism: a laboratory medicine perspective.Clinical chemistry and laboratory medicine · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to people's health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry (MS) based multi-omics has become an effective strategy to reveal the complex biological mechanisms of thyroid diseases. The exponential growth of biomedical data has promoted the applications of machine learning (ML) techniques to address new challenges in biology and clinical research. In this review, we presented the detailed review of applications of ML for MS-based multi-omics in thyroid disease. It is primarily divided into two sections. In the first section, MS-based multi-omics, primarily proteomics and metabolomics, and their applications in clinical diseases are briefly discussed. In the second section, several commonly used unsupervised learning and supervised algorithms, such as principal component analysis, hierarchical clustering, random forest, and support vector machines are addressed, and the integration of ML techniques with MS-based multi-omics data and its application in thyroid disease diagnosis is explored.
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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 OpenQuestion graph.