Classification of Microarray Data Using Subspace Grids with Synergistic and Distributed Neural Network Models

Synergistic and distributed neural network models are employed in this work for Microarray data classification. The proposed approach uses subspace grids as input to synergistic and distributed neural network models. The paper first describes projection of multidimensional Microarray data to a number of lower dimensional subspaces. This work makes use of two algorithms to define lower dimensional subspaces. The range of value associated with each vector of a subspace is divided into a number of equal parts to define subspace grids. The resulting subspace grid data is used with the proposed synergistic and distributed neural network models to classify patterns associated with multidimensional Microarray data. The results show that the use of subspaces grids with synergistic and distributed neural network models produces good results to classify patterns in multidimensional Microarray data.