Project Description

A/Prof. Nicola Armstrong

Murdoch University

Machine learning for bioinformatics

Data science and machine learning for bioinformatics

Thursday 4 July 2019

A/Prof. Armstrong is a statistical bioinformatician who completed her doctoral studies in Statistics at the University of California, Berkeley. After graduating with her PhD, she spent several years in the Netherlands as a postdoc at Eurandom and the Vrije Universiteit before moving to the Netherlands Cancer Institute in Amsterdam as a senior statistician. On returning to Australia, she worked at the Garvan Institute and the University of Sydney before moving to Murdoch University where she is currently an Associate Professor in mathematics and statistics. Her research work has centered on the development of statistical methodology and the application of statistics to problems in genetics, genomics and biomedical research.

In this talk, I will introduce some common machine learning methods that are used with ‘omics data. The advantages and disadvantages of various approaches to supervised learning will be outlined. Quantifying the performance of a technique and other important concepts that should be considered before starting to analyse data will also be discussed.