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Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey
Linjing Liu1, Xingjian Chen1, Olutomilayo Olayemi Petinrin1
1Department of Computer Science, City University of Hong Kong, Hong Kong, China.
Life (Basel, Switzerland)
|July 2, 2021
Summary
Liquid biopsy advances reveal body fluids hold cancer biomarkers. Machine learning analyzes complex biomarker data to identify tumor origins and improve cancer diagnostics.
Area of Science:
- Biomarker Discovery
- Cancer Research
- Computational Biology
Background:
- Liquid biopsy technologies increasingly identify biomarkers in bodily fluids like blood, urine, and saliva for tumor origin.
- Traditional correlation analyses are insufficient for complex biomarker-subtype relationships in cancer.
Purpose of the Study:
- To review machine learning (ML) protocols for analyzing liquid biopsy data to determine tumor origin.
- To explore ML's potential in understanding cancer mechanisms and improving diagnostics.
Main Methods:
- Review of machine learning algorithms and frameworks applied to liquid biopsy data.
- Inclusion of code demonstrations for discussed ML approaches.
Main Results:
- Machine learning offers advanced methods to analyze high-resolution liquid biopsy data.
- ML can capture complex relationships between biomarkers and cancer subtype heterogeneity.
Conclusions:
- Machine learning is crucial for exploring tumor origins using liquid biopsy data.
- Future prospects include enhanced biomarker exploration and cancer diagnostics through ML.
