GENE-CBR: A case-based reasonig tool for cancer diagnosis using microarray data sets
Gene expression profiles are composed of thousands of genes at the same time, representing the complex relationships between them. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of design methods capable of overcoming current limitations of state-of-the-art algorithms is crucial to the development of successful applications. This paper presents GENE-CBR, a hybrid model that can perform cancer classification based on microarray data. The system employs a case-based reasoning model that incorporates a set of fuzzy prototypes, a growing cell structure network and a set of rules to provide an accurate diagnosis. The hybrid model has been implemented and tested with microarray data belonging to bone marrow cases from forty-three adult patients with cancer plus a group of six cases corresponding to healthy persons.
cancer diagnosis microarray data sets hybrid model gene-cbr fuzzy patterns gene selection