Dissertation defence (Computer Science): FM Ville Laitinen


6.10.2023 at 12.00 - 16.00
FM Ville Laitinen defends his dissertation in Computer Science entitled “Statistical signatures for adverse events in molecular life sciences” at the University of Turku on 6 October 2023 at 12.00 pm (University of Turku, Natura, Lecture Hall X, Turku).

The audience can participate in the defence by remote access: https://echo360.org.uk/section/7176492f-3573-4bea-b93b-7920db10c14c/public

Opponent: Associate Professor Christopher Quince (University of Warwick, United Kingdom)
Custos: Professor Leo Lahti (University of Turku)

Doctoral Dissertation at UTUPub: https://urn.fi/URN:ISBN:978-951-29-9432-8


Summary of the Doctoral Dissertation:

The topic of the thesis is computational modeling of human microbiomics and it consists of two complementary parts.

The first part entails a pioneering study of human gut microbiome based survival analysis. The study was conducted as a part of the FINRISK cohort study and examines stool samples collected from a large random sample of the Finnish population. This study shows, for the first time, that the gut microbiome can serve as a biomarker for the overall health of the host. Furthermore, it can be used to quantify the risk for all-cause mortality. These results provide fundamental information about human health and have potential to aid in characterizing overall well-being and even inform the development of therapeutic interventions.

The second part of the thesis is methodologically oriented and concentrates on the analysis of stability properties of dynamical systems, such as microbial communities. This part introduces the Bayesian statistical framework in predicting catastrophic state transitions in such systems. These transitions, which have been reported in several real systems ranging from natural to social systems, can be anticipated by identifying statistical indicators known as early warning signals. However, predicting such events is known to be a challenge. The methodological approach introduced in the thesis enhances the ability to detect early warning signals compared to established tools, thus providing more reliable means for monitoring and potential for early intervention.
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