Thesis supervisor: Alija Pasic
Location of studies (in Hungarian): Department of Telecommunications and Media Informatics Abbreviation of location of studies: TMIT
Description of the research topic:
Fueled by the rapid development of data collection and data storage capabilities in recent decades, many scientific fields now possess an abundance of valuable data, which – due to its size, type and nature – poses new challenges for data processing and analysis. To meet these demands, the new scientific field of data science has emerged, which combines and enhances traditional data processing techniques with new, machine-learning and artificial intelligence based approaches in order to foster comprehensible and valuable work with big data. In medicine in particular, the specific data collection, storage and utilization needs which stem from privacy issues and decision support demands have created a strong need for new and modern solutions such as data lakes, cloud storage and AI-aided DS systems. Network-type information in particular – such as biological networks describing protein interactions and metabolic pathways or disease networks – is progressively gaining momentum, with the rise of a new scientific discipline called “network medicine” applying network science methodology on biomedical data towards identifying, preventing and treating diseases. Novel approaches derived from network theory and computational biology combined with large-scale datasets continue to provide new insights into the complex processes involved in human physiology and diseases. The field of network medicine encompasses not just the utilization of network topology and network dynamics towards identifying diseases and developing medical drugs, but also improving our understanding of networks inside the human body and developing new network visualization and integration techniques. Combining the network-based approach to medicine with big data and data science technologies is a very new, but promising and growing field with plenty of actuality and exciting opportunities.
Research tasks will include a review of the literature and current state-of-the-art in network medicine and data science approaches, the application of existing network science and data science paradigms to the analysis of medical data and networks, as well as the development of new, AI and ML-based solutions for the description and discovery of relationships in biomedical networks.
Required language skills: angol Number of students who can be accepted: 1
Deadline for application: 2023-01-10
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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