Author details

a potentially valuable research direction might be in the form of highly advanced probabilistic graphical models [53] augmented with functionality such as one-shot learning [54] and probabilistic program synthesis [55], which could potentially allow researchers to reduce the size of the commonly massive training datasets required for creating ANN- or DL-based models.

Furthermore, with a single exception, all of the studies reviewed here have been focused on the performance and accuracy of software ML models, which is currently the predominant class of machine learning solutions. However, recent advances in general purpose computation using both graphics processing units (GPUs) and specialised application-specific integrated circuits (ASICs) tailor-made for machine learning [56] provide a strong case for the exploration and exploitation of hardware or hybrid ML solutions, as evidenced by, e.g., the results from the

Liquid biopsy-based approaches open many so far little explored and promising opportunities for studying and measuring biological and biochemical markers with broad applications for the monitoring, diagnosis, and prognosis of a large class of diseases and processes. Machine learning, with its advanced pattern recognition capabilities, will likely play an increasingly important role in these fields, as the amount and complexity of data produced by scientific and medical sources already by far exceeds the capacity of unaided human experts and is rapidly

In addition, machine learning tools form a natural synergy with distributed, highly parallel, or cloud-based computation solutions, thus easily yielding to collaboration among researchers and medical professionals from distant locations and involving amounts of data storage and processing power previously available only on dedicated high performance computing (HPC) platforms and supercomputers. It is likely that in the near future the importance of decentralised collaboration will continue to grow, increasing the demand for powerful and

Based on these trends, we expect that the next generation of liquid biopsy technologies will include many types of machine learning as an integral part of their operation and that this trend could have a significant positive impact on both diagnosis and treatment of patients.

The present work was carried out within the frame of scientific project № 1.1.1.2 VIAA 1 16 242.

The authors declare that the chapter was written in the absence of any commercial or financial

AlphaGo experiments and public performance [57].

increasing with no foreseeable slowdown.

Acknowledgements

Conflict of interest

easy to use toolset for analysis and processing of biological data.

relationships that could be construed as a potential conflict of interest.

7. Conclusions

60 Liquid Biopsy

Arets Paeglis<sup>1</sup> \*, Boriss Strumfs<sup>2</sup> , Dzeina Mezale<sup>1</sup> and Ilze Fridrihsone<sup>1</sup>

\*Address all correspondence to: arets.paeglis@protonmail.com

