Liquid biopsies of cancer patients through the lens of machine learning – current challenges
Anna Supernat
Center of Biostatistics and Bioinformatics, Medical University of Gdańsk
Laboratory of Translational Oncology, Institute of Medical Biotechnology and Experimental Oncology, Medical University of Gdańsk, Gdańsk
The volume of research, the amount of generated data, and the numbers of patients per individual doctor constantly expand in medicine. Numerous reports indicate occupational burnout of clinicians caused by tedious work and a multitude of data, while the major causes of death - cardiovascular disease and cancer - continue to rise. The aforementioned problems can be addressed by introducing modern, non-invasive, artificial intelligence-driven liquid biopsy tests. This extended morphology, powered by machine learning, is likely to dominate the clinical practice of the future. The remaining research questions are as follows: How do we accelerate disease detection? How do we monitor disease progression? How do we predict therapy response?
The use of data to increase the efficiency of the operation of sea-going vessels
Wojciech Górski
Director of Research & Production Division, Enamor, Gdynia
Sea-going vessels can be considered a complex system of many interactions and relations. Ensuring efficient ship operation is hardly possible without continuous monitoring of the vessel state. However, due to the system complexity and the resulting vast amount of data performing this task manually by a crew is insufficient. So-called ship performance systems greatly simplify the task. In the presentation example of data collection, processing and analytics aimed at improvement of vessel efficiency is provided. The application of data engineering and machine learning are discussed in detail.
