[Machine Learning Applications in Cancer Genome Medicine]

Area of Science:

• Bioinformatics
• Clinical Oncology
• Genomics

Background:

• Cancer genome medicine relies on analyzing diverse biological data, including mutations, methylation, and gene expression.
• Machine learning (ML) algorithms are crucial for bioinformatics in clinical oncology.
• Deep learning offers technical advantages for complex biological data analysis.

Purpose of the Study:

• To review the applications of ML algorithms in recent cancer genome medicine research.
• To introduce the relationship between artificial intelligence (AI) and ML.
• To highlight the advantages of deep learning in this field.

Main Methods:

• Review of recent research publications in cancer genome medicine.
• Examination of ML applications across three key domains.
• Analysis of studies focusing on actionable mutations, Ras pathway, and cell-free DNA detection.

Main Results:

• Identified comprehensive research on actionable mutations.
• Presented a novel approach for identifying activated Ras pathways.
• Highlighted feasible methodologies to enhance cancer detection sensitivity using cell-free DNA.

Conclusions:

• ML algorithms are integral to advancing cancer genome medicine.
• The reviewed studies demonstrate ML's potential in diagnostics and therapeutic target identification.
• Further research leveraging ML can improve early cancer detection and personalized treatment strategies.