For more than a decade, machine learning (ML) and deep learning (DL) techniques have been a mainstay in the toolset for the analysis of large amounts of weakly correlated or high-dimensional data. As new technologies for detecting and measuring biochemical markers from bodily fluid samples (e.g., microfluidics and labs-on-a-chip) revolutionise the industry of diagnostics and precision medicine, the heterogeneity and complexity of the acquired data present a growing challenge to their interpretation and usage. In this chapter, we attempt to review the state of ML and DL fields as applied to the analysis of liquid biopsy data and summarise the available corpus of techniques and methodologies.
Part of the book: Liquid Biopsy