Show simple item record

dc.creatorSmolander, Johannes
dc.date.accessioned2016-03-23T09:41:58Z
dc.date.available2016-03-23T09:41:58Z
dc.identifier.urihttp://dspace.cc.tut.fi/dpub/handle/123456789/23845
dc.description.abstractIn this MSc thesis we studied how deep learning methods can be applied to class prediction of complex disorder tasks with gene expression data. Microarrays and sequencing can generate representations of expression of different biomolecular molecules in samples. With appropriate machine learning methods and data we can build classifiers that can handle various classisscation tasks with many practical applications. Deep belief networks are our principal deep learning models. We carried out tests to see how they perform alone and with support vector machines combined. We compared two different optimization algorithms, backpropagation and resilient backpropagation, that are used at the fine-tuning stage of learning. The three example data sets are composed of lung cancer, breast cancer and inflammatory bowel disease samples. For assessment of performance we used leave-one-out cross-validation with accuracy, sensitivity and specificity as performance metrics. Moreover, we computed the standard error of the mean for each metric. In order to make the results more credible and interesting, we compared them with similar previous studies. Our cross-validation results and comparison with previous studies show that we achieved good or excellent performances for most of the tasks. A remarkable aspect is that we in general omitted prior use of feature selection and dimensionality reduction that have been used previously almost invariably. The resilient backpropagation algorithm worked with whole microarray data sets, whereas the basic backpropagation algorithm worked well with whole RNA-Seq data sets and feature selected data.en
dc.format.extentix, 67
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.rightsThis publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
dc.titleDeep Learning Classification Methods for Complex Disordersen
dc.title.alternativeSyväoppimisen menetelmät kompleksisten sairauksien luokittelussa
dc.identifier.urnURN:NBN:fi:tty-201603233752
dc.contributor.laitosKemian ja biotekniikan laitos – Department of Chemistry and Bioengineeringen
dc.contributor.tiedekuntaLuonnontieteiden tiedekunta – Faculty of Natural Sciencesen
dc.contributor.yliopistoTampereen teknillinen yliopisto - Tampere University of Technology
dc.programmeBiotekniikan koulutusohjelmaen
dc.date.published2016-04-06
dc.contributor.laitoskoodikeb
dc.permissionPermission granteden
dc.contributor.thesisadvisorEmmert-Streib, Frank
dc.contributor.degreesupervisorEmmert-Streib, Frank
dc.type.ontasotDiplomityö - Master's thesis


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record