Identifying Molecular Biomarkers for Diseases With Machine Learning Based on Integrative Omics

Area of Science:

• Biomedical Informatics
• Computational Biology
• Genomics

Background:

• High-throughput technologies have generated vast amounts of molecular omics data (e.g., transcriptomics, proteomics).
• Molecular biomarkers are crucial for disease diagnosis and prognosis.
• Computational approaches are increasingly used to identify biomarkers from omics data.

Purpose of the Study:

• To provide a comprehensive review of machine learning approaches for molecular biomarker identification.
• To categorize existing machine learning methods for biomarker discovery.
• To discuss challenges and future directions in the field.

Main Methods:

• Systematic review of machine learning techniques applied to omics data for biomarker identification.
• Categorization of machine learning approaches into supervised, unsupervised, and recommendation methods.
• Analysis of identified biomarkers, including single genes, gene sets, and gene networks.

Main Results:

• Machine learning offers powerful tools for identifying molecular biomarkers from complex omics datasets.
• Supervised, unsupervised, and recommendation approaches provide diverse strategies for biomarker discovery.
• The review highlights the potential of machine learning in advancing disease diagnosis and prognosis.

Conclusions:

• Machine learning plays a pivotal role in leveraging omics data for biomarker discovery.
• Addressing challenges in biomedical data is essential for optimizing machine learning-based biomarker identification.
• Future research should focus on refining computational methods and exploring novel biomarker types.