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Semi-supervised Classification of Breast Cancer Expression Profiles Using Neural Networks

Moll, Anton G. (2018) Semi-supervised Classification of Breast Cancer Expression Profiles Using Neural Networks. PhD, Universität Regensburg.

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Dissertation of Anton G. Moll
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Date of publication of this fulltext: 16 Feb 2018 08:22

Abstract (English)

In classification tasks of biological data, there are usually fewer labeled than unlabeled samples because labeling samples is costly or time-consuming. In addition, labeled data sets can be re-used in different contexts as additional unlabeled data sets. For example, when searching the Gene Expression Omnibus (GEO) repository for microarray data sets of drug sensitivity and resistance ...

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Translation of the abstract (German)

Bei der Klassifikation von biologischen Daten stehen normalerweise weniger beschriftete als unbeschriftete Proben zur Verfügung, weil das Beschriften teuer oder zeitaufwendig ist. Außerdem können beschriftete Datensätze in einem anderen Kontext als zusätzliche unbeschriftete Datensätze wiederverwendet werden. Wenn man beispielsweise die Gene Expression Omnibus (GEO) Datenbank nach ...

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Item type:Thesis of the University of Regensburg (PhD)
Date:16 February 2018
Referee:Prof. Dr. Rainer Spang
Date of exam:25 January 2018
Institutions:Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Keywords:gene expression; neural networks; deep belief network; autoencoder; restricted boltzmann machine; transductive support vector machine; classification; semi-supervised machine learning; graphical models; breast cancer
Dewey Decimal Classification:000 Computer science, information & general works > 004 Computer science
500 Science > 500 Natural sciences & mathematics
500 Science > 570 Life sciences
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Yes
Item ID:36753
Owner only: item control page

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