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.
Highlights
When cells mutate, they could divide uncontrollably and eventually form cancer [1].According to the World Health Organization, cancer accounts for nearly 10 million deaths in 2020
When comparing machine learning models using paired t-test, we firstly evaluate each model on the same k-fold cross-validation split of the dataset and compute a performance score for each split
From the perspective of machine learning, we find out that even simple machine learning algorithms such as linear models can lead to a high-quality performance for liquid biopsy-based diagnosis for several common cancer types
Summary
They could divide uncontrollably and eventually form cancer [1]. According to the World Health Organization, cancer accounts for nearly 10 million deaths in 2020. This number is estimated to be still climbing in the following decades and will reach 27 million new cases in 2040 [2]. As the second factor of death, cancer accounts for one-sixth of deaths worldwide each year [3]. Fighting against cancer is a huge challenge for global public health. Detection, followed by tailored site-specific treatment, plays an important role in the front-line cure of cancer and could reduce the eventual mortality of cancer patients [4,5,6]
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