A framework for microarray data-based tumor diagnostic system with improving performance incrementally

Gene expression data obtained from DNA microarrays have shown useful in tumor classification problems. However, most existing related literatures focused on how to extract tumor-related genes and design appropriate classification strategies, but neglected effect of future unlabeled samples which are expensive to label. In this paper, we propose a novel framework to construct microarray data-based tumor diagnostic system with improving performance incrementally. Through the proposed framework, system is permitted to evaluate confidences of a new unlabeled sample in each class and opportunity of misdiagnosis decreases by returning uncertain samples to medical experts. Moreover, the system is also enabled to improve predictive accuracy by learning new experiences from incremental labeled samples constantly. The proposed framework of system has been tested on two well-known tumor microarray datasets with encouraging results and shown great potential for the developments of generic platform for tumor clinical diagnosis based on microarray data.