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

Abstract

Acute leukemia classification into its myeloid and lymphoblastic subtypes is usually accomplished according to the morphology of the tumor. Nevertheless, the subtypes may have similar histopathological appearance, making screening procedures difficult. In addition, approximately one-third of acute myeloid leukemias are characterized by aberrant cytoplasmic localization of nucleophosmin (NPMc(+)), where the majority has a normal karyotype. This work is based on two DNA microarray datasets, available publicly, to differentiate leukemia subtypes. The datasets were split into training and test sets, and feature selection methods were applied. Artificial neural network classifiers were developed to compare the feature selection methods. For the first dataset, 50 genes selected using the best classifier was able to classify all patients in the test set. For the second dataset, five genes yielded 97.5% accuracy in the test set.

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony

“…Up to now, many machine learning algorithms have been applied to the classification study of bioinformatics, such as random forest [ 9 ], k-nearest neighbor, neural network [ 10 ], and SVM (Support Vector Machine). Besides these, there are also some ensemble classifiers [ 11 , 12 , 13 , 14 ].…”