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Natasa Przulj Inaugural Lecture
Wed 5 April 2017, 17:00 – 19:00 BST
Natasa Przulj, Professor of Biomedical Data Science
Introduced by Professor Steve Hailes, acting Head of UCL Computer Science
Vote of Thanks by Professor Harry Hemingway, Director, Farr Institute of Health Informatics
A drinks reception will follow in Computer Science Department
We are faced with a flood of molecular and clinical data. Various biomolecules interact in a cell to perform biological function, forming large, complex systems. Large amounts of patient-specific datasets are available, providing complementary information on the same disease type. The challenge is how to mine these complex data systems to answer fundamental questions, gain new insight into diseases and improve therapeutics. Just as computational approaches for analysing genetic sequence data have revolutionized biological understanding, the expectation is that analyses of networked “omics” and clinical data will have similar ground-breaking impacts. However, dealing with these data is nontrivial, since many questions we ask about them fall into the category of computationally intractable problems, necessitating the development of heuristic methods for finding approximate solutions.
We develop methods for extracting new biomedical knowledge from the wiring patterns of large networked biomedical data, linking network wiring patterns with function and translating the information hidden in the wiring patterns into everyday language. We introduce a versatile data fusion (integration) framework that can effectively integrate somatic mutation data, molecular interactions and drug chemical data to address three key challenges in cancer research: stratification of patients into groups having different clinical outcomes, prediction of driver genes whose mutations trigger the onset and development of cancers, and re-purposing of drugs for treating particular cancer patient groups. Our new methods stem from network science approaches coupled with graph-regularised non-negative matrix tri-factorization, a machine learning technique for co-clustering heterogeneous datasets. We apply our methods to other domains, including tracking the dynamics of the world trade.
Natasa Przulj is a Professor of Biomedical Data Science at UCL Computer Science Department. She was previously a Reader (2012-2016) and Lecturer (2009-2012) in the Department of Computing at Imperial College London and an Assistant Professor in the Computer Science Department at University of California Irvine (2005-2009). She obtained a PhD in Computer Science from University of Toronto in 2005.
Professor Przulj is a Fellow of the British Computer Society. In 2014, she was awarded the British Computer Society Roger Needham Award for a distinguished research contribution in computer science by a UK based researcher within ten years of their PhD. In 2013, she was elected into the Young Academy of Europe. She received a prestigious European Research Council (ERC) Starting Independent Researcher Grant for 2012-2017 for her project titled “Biological Network Topology Complements Genome as a Source of Biological Information.” She held a prestigious NSF CAREER Award for the project titled “Tools for Analyzing, Modeling, and Comparing Protein-Protein Interaction Networks” in 2007-2011 at University of California Irvine. Her research has also been supported by other large governmental and industrial grants including those from GlaxoSmithKline, IBM and Google.