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Analysis of microarray leukemia data using an efficient MapReduce-based K-nearest-neighbor classifier.

Abstract：

:Microarray-based gene expression profiling has emerged as an efficient technique for classification, prognosis, diagnosis, and treatment of cancer. Frequent changes in the behavior of this disease generates an enormous volume of data. Microarray data satisfies both the veracity and velocity properties of big data, as it keeps changing with time. Therefore, the analysis of microarray datasets in a small amount of time is essential. They often contain a large amount of expression, but only a fraction of it comprises genes that are significantly expressed. The precise identification of genes of interest that are responsible for causing cancer are imperative in microarray data analysis. Most existing schemes employ a two-phase process such as feature selection/extraction followed by classification. In this paper, various statistical methods (tests) based on MapReduce are proposed for selecting relevant features. After feature selection, a MapReduce-based K-nearest neighbor (mrKNN) classifier is also employed to classify microarray data. These algorithms are successfully implemented in a Hadoop framework. A comparative analysis is done on these MapReduce-based models using microarray datasets of various dimensions. From the obtained results, it is observed that these models consume much less execution time than conventional models in processing big data.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Kumar M,Rath NK,Rath SK

doi

10.1016/j.jbi.2016.03.002

subject

Has Abstract

pub_date

2016-04-01 00:00:00

pages

395-409

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(16)00042-3

journal_volume

60

pub_type

杂志文章

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