Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Cancer progression involves gene-environment interactions disrupting cellular balance.
• Biomarkers are crucial for early cancer detection, diagnosis, and treatment enhancement.
• Traditional statistical methods for biomarker identification have limitations with complex biological data.

Purpose of the Study:

• To review recent advancements in machine learning (ML) and deep learning (DL) for cancer biomarker discovery.
• To highlight the integration of ML/DL methods with multi-omics data for robust prediction models.
• To discuss the application of these computational approaches in precision medicine for cancer treatment.

Main Methods:

• Utilizing large-scale omics datasets (e.g., RNA sequencing, mass spectrometry).
• Applying advanced statistical techniques and machine learning algorithms.
• Developing ensemble-learning prediction models through ML/DL integration.
• Focusing on deep learning as a key component of machine learning.

Main Results:

• ML/DL methods offer powerful tools for data-driven biomarker discovery from complex omics data.
• Integrative ML/DL approaches enhance the accuracy and robustness of predictive models.
• These methods address the challenges posed by genomic heterogeneity and epigenetic changes in cancer.

Conclusions:

• ML/DL are pivotal in discovering novel cancer biomarkers from multi-omics data.
• These computational strategies are essential for advancing precision medicine in oncology.
• The review underscores the potential of ML/DL to transform cancer diagnosis and treatment paradigms.