Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases

Area of Science:

• Biomedical data analysis
• Computational biology
• Endocrinology

Background:

• Thyroid diseases pose a significant health burden, necessitating accurate and timely diagnostic methods.
• Mass spectrometry (MS)-based multi-omics, particularly proteomics and metabolomics, provides insights into disease mechanisms.
• The increasing volume of biomedical data necessitates advanced analytical techniques.

Purpose of the Study:

• To review the applications of machine learning (ML) for MS-based multi-omics in thyroid disease research.
• To explore the integration of ML with multi-omics data for improved thyroid disease diagnosis.

Main Methods:

• Discussion of MS-based multi-omics techniques, including proteomics and metabolomics.
• Overview of commonly used ML algorithms: unsupervised (e.g., principal component analysis, hierarchical clustering) and supervised (e.g., random forest, support vector machines).
• Exploration of ML integration with MS-based multi-omics data.

Main Results:

• ML techniques are effective in analyzing complex MS-based multi-omics data.
• The integration of ML with proteomics and metabolomics data shows promise for thyroid disease diagnosis.
• Specific ML algorithms are highlighted for their utility in this domain.

Conclusions:

• ML applied to MS-based multi-omics represents a promising strategy for advancing thyroid disease diagnosis.
• Further research integrating these technologies can lead to more accurate and efficient diagnostic tools.
• This approach aids in understanding the complex biological underpinnings of thyroid pathologies.