Non-negative matrix factorization in cancer omics

• PhD
• Masters by Coursework
• Honours

Non-negative matrix factorisation (NMF) is an unsupervised machine learning technique for identifying patterns in data. NMF has a long history of use in bioinformatics and a number of NMF methods have been developed specifically for omics data. This also includes semi-supervised approaches to NMF.

However, how to best apply unsupervised or semi-supervised approaches is currently not clear. This project will deliver a benchmark of NMF methods, and develop new methods and guidelines to make these tools more accessible and their results more interpretable.

The Papenfuss Lab develops and applies mathematical, statistical and computational methods to analyze and make sense of complex molecular and clinical data from cohorts of patients with cancer.

Our overarching goal is to enable insights into cancer evolution, including initiation, progression, therapy response and outcome.