Artificial Neural Networks and Ranking Approach for Probe Selection and Classification of Microarray Data

Acute leukemia classification into its Myeloid and Lymphoblastic subtypes is usually accomplished according to the morphological appearance of the tumor. Nevertheless, cells from the two subtypes can have similar histopathological appearance, which makes screening procedures very difficult. Correct classification of patients in the initial phases of the disease would allow doctors to properly prescribe cancer treatment. Therefore, the development of alternative methods, to the usual morphological classification, is needed in order to improve classification rates and treatment. This paper is based on the principle that DNA microarray data extracted from tumors contain sufficient information to differentiate leukemia subtypes. The classification task is described as a general pattern recognition problem, requiring initial representation by causal quantitative features, followed by the construction of a classifier. In order to show the validity of our methods, a publicly available dataset of acute leukemia comprising 72 samples with 7,129 features was used. The dataset was split into two subsets: the training dataset with 38 samples and the test dataset with 34 samples. Feature selection methods were applied to the training dataset. The 50 most predictive genes, according to each method, were selected. Artificial Neural Network (ANN) classifiers were developed to compare the feature selection methods. Among the 50 genes selected using the best classifier, 21 are consistent with previous work and 4 additional ones are clearly related to tumor molecular processes. The remaining 25 selected genes were able to classify the test dataset, correctly, using the ANN.