7. Conclusions

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 increasing with no foreseeable slowdown.

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

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.
