Histopathologcial and clonal study of combined lobular and ductal carcinoma of the breast.

Lobular carcinoma in situ (LCIS) clinically constitutes a risk factor for the subsequent development of either invasive lobular carcinoma (ILC) or invasive ductal carcinoma (IDC). In order to approach the possibility of this common precursor of both ILC and IDC, we investigated combined lobular and ductal carcinomas. Thirty-two cases of lobular carcinoma were picked up out of 773 cases of operated breast carcinomas.

Serum cytokine profiles in patients with prostate carcinoma.

It has been suggested that various cytokines are associated with the pathophysiology of prostate carcinoma (Pca). We profiled ten cytokines (IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IFN-γ and TNF-α) in the serum levels of 11 patients with organ-confined Pca, 15 with advanced Pca without cachexia, 8 with advanced Pca with cachexia (cachexia group) and 5 healthy males as controls. Cytokines were measured using a highly sensitive fluorescence microsphere system.

Template learning of cellular neural network using genetic programming.

A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions.

Rough sets and genetic algorithms in learning cellular neural networks cloning template for decision making system.

We purpose to find a new beneficial method for accelerating the Decision-Making and classifier support applied on imprecise data. This acceleration can be done by integration between Rough Sets theory, which gives us the minimal set of decision rules, and the Cellular Neural Networks. Our method depends on Genetic Algorithms for designing the cloning template for more accuracy.

Decision making using hybrid rough sets and neural networks.

A methodology for using rough sets theory for preference modeling in decision problem is presented in this paper. We will introduce a new method where neural network systems and rough sets theory are completely integrated into a hybrid system and are used cooperatively for decision and classification support. At the first glance, the two methods we discuss have not much in common.