Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders

Johannes Smolander, Matthias Dehmer, Frank Emmert-Streib

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

26 Zitate (Scopus)

Abstract

Genomics data provide great opportunities for translational research and the clinical practice, for example, for predicting disease stages. However, the classification of such data is a challenging task due to their high dimensionality, noise, and heterogeneity. In recent years, deep learning classifiers generated much interest, but due to their complexity, so far, little is known about the utility of this method for genomics. In this paper, we address this problem by studying a computational diagnostics task by classification of breast cancer and inflammatory bowel disease patients based on high-dimensional gene expression data. We provide a comprehensive analysis of the classification performance of deep belief networks (DBNs) in dependence on its multiple model parameters and in comparison with support vector machines (SVMs). Furthermore, we investigate combined classifiers that integrate DBNs with SVMs. Such a classifier utilizes a DBN as representation learner forming the input for a SVM. Overall, our results provide guidelines for the complex usage of DBN for classifying gene expression data from complex diseases.

OriginalspracheEnglisch
Seiten (von - bis)1232-1248
Seitenumfang17
FachzeitschriftFEBS Open Bio
Jahrgang9
Ausgabenummer7
DOIs
PublikationsstatusVeröffentlicht - Juli 2019

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