Clustering Cancer Tumours using Unsupervised Deep Learning Techniques

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dc.contributor.authorLilja, Oskar
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mathematical Sciencesen
dc.date.accessioned2019-07-03T14:21:01Z
dc.date.available2019-07-03T14:21:01Z
dc.date.issued2016
dc.description.abstractThe 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.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/242825
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectKlinisk medicin
dc.subjectGrundläggande vetenskaper
dc.subjectMedicinska grundvetenskaper
dc.subjectMatematik
dc.subjectFysik
dc.subjectClinical Medicine
dc.subjectBasic Sciences
dc.subjectBasic Medicine
dc.subjectMathematics
dc.subjectPhysical Sciences
dc.titleClustering Cancer Tumours using Unsupervised Deep Learning Techniques
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeApplied physics (MPAPP), MSc
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