Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Overview

Journal Life (Basel)

Specialty Biology

Date 2021 Jul 2

PMID 34209249

Citations 19

Authors

Linjing Liu

Xingjian Chen

Olutomilayo Olayemi Petinrin

Weitong Zhang

Saifur Rahaman

Zhi-Ri Tang

Ka-Chun Wong

Affiliations

Soon will be listed here.

Abstract

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.

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Liquid biopsy into the clinics: Current evidence and future perspectives.

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