Data mining can find cancer cures
A flood of molecular and clinical data is now available describing how biomolecules interact in a cell to perform biological functions, in large, complex systems. Our challenge is how to mine the enormous data bases describing molecular systems to answer fundamental questions, gain new insight into diseases and improve therapeutics.
Computational approaches for analysing genetic sequence data have revolutionized biological understanding, and the expectation is that analyses of networked “omics” data will have similar ground-breaking impacts. Nevertheless, analysing these data is nontrivial, since many questions we ask about them represent computationally intractable problems, necessitating the development of heuristic methods for finding approximate solutions.
We have developed methods for extracting new biomedical knowledge from the wiring patterns of large networked biomedical data bases, 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.
The talk will cover developments and experience in this emerging and critically important field.
Our speaker: Natasa Przulj, UCL
Dr. Natasa Przulj is a Professor at the Computer Science Department at UCL. Prior to being appointed professor at UCL, Dr Przulj was an Associate Professor at Imperial, where she was also a member of the Institute of Systems and Synthetic Biology, the Centre for Bioinformatics, and the Centre for Integrative Systems Biology (CISBIC). She was an Assistant Professor in the Department of Computer Science at University of California Irvine from 2005 to 2010. She obtained a PhD in Computer Science from University of Toronto, Canada, in 2005.
Dr. 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.
Dr. Przulj is widely recognized for initiating extraction of biological knowledge purely from topology of real-world networks. That is, she views large and complex biological networks as a new source of biological information that needs to be mined, and looks for links between network topology in protein-protein interaction networks and biological function and involvement of proteins in disease. Her recent work includes integration and dynamics of heterogeneous network data, applied to many areas of systems biology and medicine, as well as to economics.