"Untangling Biological Complexity: From Omics Data to Data-Integrated Medicine"
Welcome to the first BIO Seminar. The BIO seminars is an open seminar series from the Department of Biology and Biological Engineering, where we meet to listen to internationally renowned speakers from research fields relevant to BIO.
After the lecture, at 15:00, it will be possible to meet Natasa in break-out session.
About 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
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.
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 biomolecules 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
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
utilise 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
via Zoom; register for link and password
18 November, 2020, 14:00
18 November, 2020, 16:00