Artificial immune system based on syndromes-response approach: recognition of the patterns of immune response and prognosis of therapy outcome

How can one make a prediction for an individual outcome, given ill-structured and multi-scaled data about a small group of other similar individuals? To answer this question, we develop a robust combinatorial-statistical optimization method for doing pattern recognition based on such data. This method is based on the simulation of recognition processes in the immune system. Our method demonstrates higher robustness and predictive power compared to the classification and regression trees (CART) method in predicting the outcome of immunotherapy for superficial bladder cancer based on immunological measurements.