Neural network model for integration and visualization of introgressed genome and metabolite data

The volume of information derived from post-genomic technologies is rapidly increasing. Due to the amount of data involved, novel computational models are needed for introducing order into the massive data sets produced by these new technologies. Data integration is also gaining increasing attention for merging signals in order to discover unknown pathways. These topics require the development of adequate soft computing tools. This work proposes a neural network model for discovering relationships between gene expression and metabolite profiles of introgressed lines. It also provides a simple visualization interface for identification of coordinated variations in mRNA and metabolites. This may be useful when the focus is on the easily identification of groups of different patterns, independently of the number of formed clusters. This kind of analysis may help for the inference of a-priori unknown metabolic pathways involving the grouped data. The model has been used on a case study involving data from tomato fruits.