With the advent of high throughput DNA microarrays and the large cross section of the gene activity, or expression, that it provides, the potential for the early detection and diagnosis of cancer before morphogenesis has dramatically increased. While many statistical analysis methods, such as cluster analysis, have been developed to tap into this enormous information source, a reliable method of early detection and diagnosis has yet to be developed. In this paper we propose using independent component analysis (ICA) as a first step in a process to identify diseased tissue solely based on its gene expression profile. In the ICA vernacular, a set of tissues samples with a known disease can be viewed as the sensors while certain biological processes, including the manifestation of the disease, can be viewed as the signals. The goal then is to identify one or more demixed signals, or signatures, that can be associated with the given disease. The demixing matrix can then be used to find the biological signals of an unknown sample, which might, in turn, be used for diagnosis when compared to the previously determined disease signatures. In this paper we explore the use of this technique on a previously studied melanoma dataset (Bittner, et. al., 2000).