About Natasa Przulj:
Natasa Przulj is both Professor of Biomedical Data Science at Computer Science, University College London and ICREA Research Professor, Life Sciences Department, Barcelona Supercomputing Center.
She has a PhD in computer science from the University of Toronto. Prof. Przulj initiated extraction of biomedical knowledge from the wiring patterns (topology, structure) of "Big Data" real-world molecular (omics) and other networks.
She views the wiring patterns of large and complex omics networks, disease ontologies, clinical patient data, drug-drug and drug-target interaction networks etc., as a new source of information that complements the genetic sequence data and needs to be mined and meaningfully integrated to gain deeper biomedical understanding.
She received several awards and prestigious grants, among other two grants from the ERC.
We are faced with a flood of molecular and clinical data. We are measuring interactions between various bio-molecules in and around a cell that form large, complex systems. Patient omics datasets are also increasingly becoming available. These systems-level network data provide heterogeneous, but complementary information about cells, tissues and diseases. The challenge is how to mine them collectively to answer fundamental biological and medical questions. This is nontrivial, because of computational intractability of many underlying problems on networks (also called graphs), necessitating the development of approximate algorithms (heuristic methods) for finding approximate solutions.
We develop methods for extracting new biomedical knowledge from the wiring patterns of systems-level, heterogeneous biomedical networks. Our methods uncover the patterns in molecular networks and in the multi-scale network organization indicative of biological function, translating the information hidden in the network topology into domain-specific knowledge. We also introduce a versatile data fusion (integration) framework to address key challenges in precision medicine from biomedical network data: better stratification of patients, prediction of driver genes in cancer, and re-purposing of approved drugs to particular patients and patient groups, including Covid-19 patients. Our new methods stem from novel network science algorithms coupled with graph-regularized non-negative matrix tri-factorization, a machine learning technique for dimensionality reduction and co-clustering of heterogeneous datasets. We utilize our new framework to develop methodologies for performing other related tasks, including disease re-classification from modern, heterogeneous molecular level data, inferring new Gene Ontology relationships, aligning multiple molecular networks, and uncovering new cancer mechanisms.