biomarker discovery based on hybrid optimization algorithm and artificial neural networks on microarray data for cancer classification

the improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. detailed examination of changes in expression levels of genes can help physicians to have effi cient diagnosing, classification of tumors and cancer’s types as well as effective treatments. finding genes that can classify the group of cancers correctly based on hybrid optimization algorithms is the main purpose of this paper. in this paper, a hybrid particle swarm optimization and genetic algorithm method are used for gene selection and also artificial neural network (ann) is adopted as the classifier. in this work, we have improved the ability of the algorithm for the classification problem by finding small group of biomarkers and also best parameters of the classifier. the proposed approach is tested on three benchmark gene expression data sets: blood (acute myeloid leukemia, acute lymphoblastic leukemia), colon and breast datasets. we used 10-fold cross-validation to achieve accuracy and also decision tree algorithm to find the relation between the biomarkers for biological point of view. to test the ability of the trained ann models to categorize the cancers, we analyzed additional blinded samples that were not previously used for the training procedure. experimental results show that the proposed method can reduce the dimensional of the data set and confirm the most informative gene subset and improve classification accuracy with best parameters based on datasets.