Analysis and integration of biological data: a data mining approach using neural networks.

MILONE D.; STEGMAYER, G.; GERARD, M.; KAMENETZKY L.; LÓPEZ, M.; CARRARI F.

Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains,

IGI Global Publishers

Lugar: Tamilnadu. INDIA; Año: 2010; p. 287 - 314

The volume of information derived from postgenomic technologies is rapidly increasing. Due to the amount of data involved, 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 expression and metabolite variations. 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.