Statistical Bioinformatics Seminar: Thuc Le (UniSA) -- Causality Discovery and Applications in Bioinformatics and Cancer Research

For the next Statistical Bioinformatics Seminar, we will be hosting Dr Thuc Le from the
University of South Australia.  The seminar will be held at 1:00pm Monday at the Charles
Perkins Centre, Seminar Room (Level 3, large meeting room).  The format of the talk is
approximately 40 minutes plus discussion.  Further information can be found on the
website, https://www.maths.usyd.edu.au/u/SemConf/StatisticalBioinformatics.html  

Title: Causality Discovery and Applications in Bioinformatics and Cancer Research 

Abstract: In many real-world applications, the research questions of interest are about
causality rather than association, whether the goal is for better explanation,
prediction or decision making.  Causal discovery aims to answer the causality related
questions by inferring the cause-effect relationships between variables.  Traditionally,
causal relationships are identified by making use of interventions or randomised
controlled experiments.  However, conducting such experiments is often expensive or even
impossible due to cost or ethical concerns.  Therefore, there has been an increasing
interest in discovering causal relationships based on observational data, and in the
past few decades, significant contributions have been made to this field by computer
scientists.  In this talk, I will briefly introduce causality discovery approaches and
then talk about a few applications in Bioinformatics and cancer research, including
inferring miRNA regulatory relationships, predicting cancer treatment responses, and
identifying cancer drivers.  

About the speaker: Thuc is a Senior Lecturer in the School of Information Technology and
Mathematical Sciences, University of South Australia.  He is currently an NHMRC Early
Career Research Fellow in Bioinformatics (2017-2020).  His research focuses on the
development of causal inference methods and their applications in Bioinformatics,
particularly in gene regulatory networks, cancer drivers, non-coding RNAs, and cancer
subtype discovery.