@inproceedings{f4b238b5f2b74efa8631643b23becae2,
title = "Selecting informative genes from microarray data by using a cyclic GA-based method",
abstract = "Microarray data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contributes to a cancer disease. This selection process is difficult due to the availability of a small number of samples compared to the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this paper proposes a cyclic method based on genetic algorithms (GA) to select a near-optimal (small) subset of informative genes that is relevant for cancer classification. The performance of the proposed method was evaluated by three benchmark microarray data sets and obtained encouraging results as compared with other experimented methods and previous related works.",
keywords = "Component, Cyclic approach, Gene selection, Genetic algorithms, Hybrid method, Microarray data",
author = "Mohamad, {Mohd Saberi} and Sigeru Omatu and Safaai Deris and Michifumi Yoshioka",
year = "2010",
doi = "10.1109/ISMS.2010.14",
language = "English",
isbn = "9780769539737",
series = "ISMS 2010 - UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation",
pages = "15--20",
booktitle = "ISMS 2010 - UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation",
note = "UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2010 ; Conference date: 27-01-2010 Through 29-01-2010",
}
