Clustering Gene Expression Data using Neural Networks

Microarray technology can be used to collect gene expression data in bulk. In order to be able to deal with this large amount of data that can now be produced, an efficient method of computing the relationships of this data must be constructed. Some attempts at applying neural networks have been employed for this task. For this project we intend to implement several neural network architectures in hopes of determining an accurate, efficient mechanism for clustering gene expression data. The accuracy and efficiency will be measured against the results of previous experiments, and with results of other methods of clustering, such as K-means clustering.