Thesis supervisor: Péter Antal
Location of studies (in Hungarian): Department of Measurement and Information System Abbreviation of location of studies: MIT
Description of the research topic:
New molecular measurement technologies, particularly in genetics, fundamentally changed the way of biomedical research by the generation of massive amount of data. However, the slower than expected clinical utilization of these information sources led to the realization that further development in life sciences depends at least as much on the fusion of data and knowledge, as on further technological breakthroughs in data generation. Additionally, the focus has been shifting towards the collection of real-life data in very large populations, covering information about life style, environment and general health state, including detailed information about morbidities and drug usage. This collection is supported by the spread of wearable electronic devices and other supportive technologies of ambient assisted living.
This novel “big health data” provides an unprecedented opportunity for a systems-based investigation of genetic, personal, environmental and societal aspects of health, ageing and diseases.
The proposed thesis topic covers the investigation of methods to support tractable, systems-based analysis of high-dimensional health data sets from millions of patients, containing e.g. full polymorphic genetic profile, onsets of diseases and drug consumption together with environmental and life style data, such as diet and sporting. These methods will be able to explore gene-environment interactions for complex diseases and common genetic architecture for complex, related comorbidities, by the following extensions.
Bayesian approaches, such using Bayesian networks (BNs), are ideal candidate for the exploration of multivariate dependency patterns, particularly in case of systematically incomplete data, because of their comprehensive probabilistic and optionally causal interpretations. However, to scale-up these methods for high-dimensional data sets, such as for genome-wide polymorphisms data, is an open challenge.
The main application areas will be allergy, psychiatric diseases and mood disorders. This research will support the systems-based investigation of genetic, personal, environmental and societal aspects of health, ageing and diseases, hopefully leading to novel drug targets, biomarkers and more effective personalized treatments.
Required language skills: English Further requirements: C++
R, Julia
probability theory (statistics)
Number of students who can be accepted: 2
Deadline for application: 2017-06-26
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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