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Presentation
Identifying and Implementing the Underlying Operators for Nuclear Magnetic Resonance based Metabolomics Data Analysis
Third International Conference on Bioinformatics and Computational Biology (BICoB-2011)
  • Ashwin Manjunatha, Wright State University - Main Campus
  • Ajith H. Ranabahu, Wright State University - Main Campus
  • Paul E. Anderson, Wright State University - Main Campus
  • Amit P. Sheth, Wright State University - Main Campus
Document Type
Conference Proceeding
Publication Date
3-23-2011
Abstract

The science of metabolomics is a relatively young field that requires intensive signal processing and multivariate data analysis for interpretation of experimental results. The lack of integration and standardization for metabolomics compounded by the complexity of the experimental data has lead to a fragmented research community. While efforts have been undertaken to approach these problems, the efforts to develop a set of standards for reporting processing and analysis procedures has stalled.

In this paper, we propose a set of fundamental operators for nuclear magnetic resonance(NMR) based metabolomics. These operators are implementation independent, and can be used to easily and precisely describe the processing and analysis steps that led to research conclusions. This formalization can facilitate inter-lab communication, and due to its simplicity, it is easily adapted by the metabolomics community. A Domain Specific Language (DSL) is also included to demonstrate an implementation of these operators. The DSL is simple, convenient for a domain scientist, and can be easily transformed into multiple target platforms.

Comments

This paper was presented at the Third International Conference on Bioinformatics and Computational Biology, New Orleans, Louisiana, USA, March 23 - 25, 2011.

This is the authors' version of the paper.

Citation Information
Ashwin Manjunatha, Ajith H. Ranabahu, Paul E. Anderson and Amit P. Sheth. "Identifying and Implementing the Underlying Operators for Nuclear Magnetic Resonance based Metabolomics Data Analysis" Third International Conference on Bioinformatics and Computational Biology (BICoB-2011) (2011) p. 205 - 209 ISSN: 9781617823886
Available at: http://works.bepress.com/amit_sheth/30/