Application of machine learning for high-throughput tumor marker screening.

Area of Science:

• Biomedical Informatics
• Computational Biology
• Oncology

Background:

• High-throughput sequencing and multiomics generate vast amounts of biomarker data for cancer diagnosis and prognosis.
• Screening complex and numerous tumor markers presents a significant challenge in clinical practice.
• Machine learning (ML) offers advanced computational approaches to address these challenges.

Purpose of the Study:

• To review the application of machine learning (ML) in screening tumor markers.
• To discuss common ML algorithms and their role in analyzing multiomics data for cancer.
• To highlight the potential of ML in enhancing tumor marker discovery and cancer therapy.

Main Methods:

• Review of current literature on ML applications in tumor marker screening.
• Discussion of general ML processes and common algorithms.
• Analysis of ML applications across genomic, transcriptomic, proteomic, and metabolomic data.

Main Results:

• ML techniques are instrumental in recognizing complex patterns within multiomics data.
• ML enhances the efficiency and accuracy of tumor marker screening pipelines.
• ML aids in modeling dynamic disease changes and predicting patient outcomes.

Conclusions:

• ML provides powerful tools for efficient and accurate tumor marker screening.
• The integration of ML with multiomics data holds significant promise for advancing cancer diagnosis, risk stratification, and treatment.
• Future prospects involve further refining ML applications for personalized cancer therapy.