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Analysis and Integration of Biological Data: A Data Mining Approach using Neural Networks
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Author(s): Diego Milone (Universidad Nacional del Litoral & National Scientific and Technical Research Council, Argentina), Georgina Stegmayer (Universidad Tecnologica Nacional & National Scientific and Technical Research Council, Argentina), Matías Gerard (Universidad Nacional del Litoral & Universidad Tecnologica Nacional & National Scientific and Technical Research Council, Argentina), Laura Kamenetzky (Institute of Biotechnology, INTA & National Scientific and Technical Research Council, Argentina), Mariana López (Institute of Biotechnology, INTA & National Scientific and Technical Research Council, Argentina)and Fernando Carrari (Institute of Biotechnology, INTA & National Scientific and Technical Research Council, Argentina)
Copyright: 2013
Pages: 28
Source title:
Data Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-2455-9.ch011
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Abstract
The volume of information derived from post genomic technologies is rapidly increasing. Due to the amount of involved data, novel computational methods are needed for the analysis and knowledge discovery into the massive data sets produced by these new technologies. Furthermore, data integration is also gaining attention for merging signals from different sources in order to discover unknown relations. This chapter presents a pipeline for biological data integration and discovery of a priori unknown relationships between gene expressions and metabolite accumulations. In this pipeline, two standard clustering methods are compared against a novel neural network approach. The neural model provides a simple visualization interface for identification of coordinated patterns variations, independently of the number of produced clusters. Several quality measurements have been defined for the evaluation of the clustering results obtained on a case study involving transcriptomic and metabolomic profiles from tomato fruits. Moreover, a method is proposed for the evaluation of the biological significance of the clusters found. The neural model has shown a high performance in most of the quality measures, with internal coherence in all the identified clusters and better visualization capabilities.
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