Earticle

초록

영어

Microarray gene expression techniques and tools have become of a substantial importance and widely used to analyze the protein-protein interaction (PPI) and gene regulation network (GRN) research in recent years since it can capture the expressions of thousands of genes in a single experiment. Such dataset poses a great challenge for finding association rules in a faster way because of the presence of large number of columns but a small number of rows. Therefore, to meet the challenge of high volume of gene expression and the complexity of microarray data, various data mining methods and applications have been proposed for analyzing gene expressions. However, it is not trivial to extract biologically meaningful information from the huge amount of gene expression data in understanding of gene regulation networks and cellular state, because most cellular processes are regulated by changes in gene expression. Association rule mining techniques are helpful to find relationship between genes, but most of the developed association rule mining algorithms are based on main memory and single processor based techniques which are not capable of handling ever increasing large data and producing result in a faster way. In this paper, we proposed a MapReduce framework for mining association rules from a huge microarray gene expression dataset on Hadoop; which not only overcomes of the main memory bottleneck but also highly scalable in terms of increasing data size. When we apply this new method to the mice lungs and spinal cord microarray compendium data, it identifies a majority of known regulons as well as novel potential target genes of numerous key transcription factors. Extensive experimental results show that our proposed approach is efficient for mining high confident association rules from large microarray gene expression datasets in terms of time and scalability.

목차

Abstract
 1. Introduction and Motivations
 2. Problem Statement and Background Study
  2.1. Analysis of Microarray Dataset
  2.2 Cloud Technologies and the Services for Analyzing Big Dataset
  2.3 Apache Hadoop, HDFS and Relevance of MapReduce to Many-Task Computing
  2.3 Mining Association Rules
 3. Proposed MapReduce Framework
  3.1 Proposed Programming Model
  3.2 AN EXAMPLE
 4. Experimental Results
  4.1 Hardware Configurations
  4.2 Description of the Datasets
  4.3 Performance Analysis
 5. Conclusions
 Acknowledgement
 References

키워드

저자

• Md. Rezaul Karim [ Department of Computer Engineering, Kyung Hee University, Republic of Korea ]
• A. T. M Golam Bari [ Department of Computer Engineering, Kyung Hee University, Republic of Korea ]
• Byeong-Soo Jeong [ Department of Computer Engineering, Kyung Hee University, Republic of Korea ]
• Ho-Jin Choi [ Dept. of Computer Science, Korea Advanced Institute of Science and Technology, Republic of Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605