Clustering Cancer Tumours using Unsupervised Deep Learning Techniques
Typ
Examensarbete för masterexamen
Master Thesis
Master Thesis
Program
Applied physics (MPAPP), MSc
Publicerad
2016
Författare
Lilja, Oskar
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The modern technology of DNA microarrays has made high-dimensional genomic data available for large-scale analysis. This thesis investigates how unsupervised deep learning techniques may be used as a class discovery method analysing cancer tumour data. Furthermore, the possibility of inferring which genes most strongly contribute in the differentiation of cancer types is discussed. Gene expression data from The Cancer Genome Atlas of 10 different cancer tumour types are analysed. A deep autoencoder network clearly separates cancer tumours as well as known subtypes of tumours already in 2-dimensions. The results are compared with other dimensionality reduction methods like principal component analysis.
Beskrivning
Ämne/nyckelord
Klinisk medicin , Grundläggande vetenskaper , Medicinska grundvetenskaper , Matematik , Fysik , Clinical Medicine , Basic Sciences , Basic Medicine , Mathematics , Physical Sciences