Title: Ensemble classification for gene expression data based on parallel clustering

Authors: Jun Meng; Dingling Jiang; Jing Zhang; Yushi Luan

Addresses: School of Computer Science and Technology, Dalian University of Technology, Dalian, China ' School of Computer Science and Technology, Dalian University of Technology, Dalian, China ' School of Computer Science and Technology, Dalian University of Technology, Dalian, China ' School of Life Science and Biotechnology, Dalian University of Technology, Dalian, China

Abstract: Analysis of large-scale gene expression data is a research hotspot in the field of bioinformatics, which can be used to study abnormal phenomenon in plant growth process. This paper proposes a biological knowledge integration method based on parallel clustering to select gene subsets effectively. Gene ontology is utilised to obtain the biological functional similarity, and combined with gene expression data. Parallelised affinity propagation algorithm is used to cluster data since it can not only obtain more biologically meaningful subsets, but also avoid the loss of some potential value in genes from simple gene primary selection. The algorithm is verified with four typical plant datasets and compared with other well-known integration methods. Experimental results on plant stress response datasets demonstrate that the proposed method can select genes with stronger classification ability.

Keywords: ensemble classification; microarray data; MapReduce programming model; parallel information fusion.

DOI: 10.1504/IJDMB.2018.094779

International Journal of Data Mining and Bioinformatics, 2018 Vol.20 No.3, pp.213 - 229

Received: 16 May 2018
Accepted: 12 Jun 2018
Published online: 15 Sep 2018 *

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