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IJMLC 2012 Vol.2(6): 798-801 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.240

Prediction of Protein-Protein Docking Sites Based on a Cloud-Computing Pipeline

Hui Li, Jean-Claude Tounkara, and Chunmei Liu

Abstract—To predict protein-protein docking sites in a massive protein dataset, we built a cloud computing based computing pipeline. This pipeline conforms to Elastic MapReduce. The implementation of this pipeline includes three components. First, the cloud computing is based on the application of the open source hadoop platform. Second, the pipeline combines several existing protein-protein docking site methods. Third, the pipeline takes advantage of network computing resource to predict protein-protein docking sites by distributed data processing services. The results show our method can highly improve the performance of protein-protein docking site prediction.

Index Terms—Cloud-computing; protein docking site; pipeline.

The authors are with the Department of Systems and Computer Science, Howard University, Washington, DC 20059, USA. (e-mail: hli@scs.howard.edu; jeanclaudetounkara@gmail.com; chunmei@scs.howard.edu.).

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Cite:Hui Li, Jean-Claude Tounkara, and Chunmei Liu, "Prediction of Protein-Protein Docking Sites Based on a Cloud-Computing Pipeline," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 798-801, 2012.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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