Statistical learning of the mutational processes in cancer

Asger Hobolth
(BiRC, Aarhus University)
Thiele Seminar
Monday, 19 March, 2018, at 12:15-13:00, in Aud. D1 (1531-113)

In the last few years I have been involved in a major effort to enhance our understanding of the mutational processes in human cancer. A particularly popular method is an unsupervised learning method called non-negative matrix factorization (NMF). Parameter estimation in the NMF model is a difficult high-dimensional optimization problem. I will describe and compare a number of NMF optimization algorithms, including a majorize-minimize algorithm, an expectation-maximization algorithm, and a procedure based on cone projection in convex analysis. A major finding is that a mix-and-match strategy often performs better than running each algorithm in isolation.
This is joint work with Astrid Kousholt, Qianyun Guo and Jens Ledet Jensen.  

Organised by: The T.N. Thiele Centre
Contact person: Mark Podolskij