Application of neural network to gene expression data for cancer classification

The goal of the work is to explore the use of gene expression data in discriminating two types of very similar cancers-acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Classification results are reported in Golub et al. (1999) using methods other than neural networks. Here, we explore the role of the feature vector in classification. Each feature vector consists of 6817 elements which are gene expression data for 6817 genes. We show in this preliminary experiment that learning using neural networks is possible when the input vector contains the correct number of gene expression data. This result is very promising because of the nature of the data (available in large amounts and more new information becomes available with better technology and better understanding of the problem). Thus, it is absolutely essential to employ an automated recognition system that has learning capability.