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Alisa Pavel: Knowledge graphs and network models improve toxicology and pharmacology insights

Tampereen yliopisto
SijaintiArvo Ylpön katu 34, Tampere
Kaupin kampus, Arvo-rakennus, Jarmo Visakorpi -sali ja etäyhteys
Ajankohta12.4.2024 8.00–12.00
PääsymaksuMaksuton tapahtuma
Alisa Pavel seisoo punakuvioinen takki yllään peltomaisemassa. Taustalla on metsää.
Kuva: Emili Pavel
Big data analysis is an important topic in many different areas of life sciences. However, current big data studies in the life sciences are often limited to a small range of data sources and data types due to the diversity and complexity of the available data, standards, and interpretations. In her doctoral dissertation MSC Alisa Pavel constructs and applies knowledge graph-based data structures and network analysis-based methodologies to different questions in pharmacology and toxicology.

Data has many facets and applications across the life sciences. Its application can range from hospital management, patient care, phenotype classification to drug development and chemical safety assessment. Combining big data with data science can improve insights and predictions by enhancing data quality, robustness and increases the information content.

“However, large scale big data analytics in the life sciences is not as easy as in many other IT-based fields, due to the high variety of data types, data points, reporting quality, standards and even often that the data is not available in computational processable formats, which makes automated data integration into a large data model highly challenging,” states Alisa Pavel.

In her doctoral dissertation, Pavel investigates knowledge graphs in combination with computational network analysis methodologies, such as data modeling, as well as knowledge inferring engine for different applications in pharmacology and toxicology.

The dissertation presents the Unified Knowledge Space (UKS), a multi-billion data point life science Knowledge Graph which can be applied to different pharmacology and toxicology questions.

In one study non-measured genes and potential drug repositioning candidates for COVID-19 are identified while other studies investigate the comparability and behavior of chemical exposures across different biological systems.

Pavel conducted here doctoral research at the Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE) at Tampere University, Faculty of Medicine and Health Technology. She currently works at the Technical University of Denmark.

Public defence on Friday 12 April

The doctoral dissertation of MSC Alisa Pavel in the field of Bioinformatics titled Knowledge Graphs, Network Models and Health Data Science Approaches for Toxicology and Pharmacology will be publicly examined at the Faculty of Medicine and Health Technology at Tampere University at 11:00 on Friday 12 April 2024 at Kauppi campus, Arvo building, Jarmo Visakorpi -sali (Arvo Ylpön katu 34, 33520 Tampere). The Opponent will be Professor Jacques Fleuriot from The University of Edinburgh. The Custos will be Professor Dario Greco from Tampere University.

The doctoral dissertation is available online

The public defence can be followed via remote connection