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Artificial neural network model for effective cancer classification using microarray gene expression data

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Abstract

Microarray gene expression profile shall be exploited for the efficient and effective classification of cancers. This is a computationally challenging task because of large quantity of genes and relatively small amount of experiments in gene expression data. The repercussion of this work is to devise a framework of techniques based on supervised machine learning for discrimination of acute lymphoblastic leukemia and acute myeloid leukemia using microarray gene expression profiles. Artificial neural network (ANN) technique was employed for this classification. Moreover, ANN was compared with other five machine learning techniques. These methods were assessed on eight different classification performance measures. This article reports a significant classification accuracy of 98% using ANN with no error in identification of acute lymphoblastic leukemia and only one error in identification of acute myeloid leukemia on tenfold cross-validation and leave-one-out approach. Furthermore, models were validated on independent test data, and all samples were correctly classified.

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Acknowledgements

The authors are extremely thankful to department of Biotechnology, New Delhi for providing Bioinformatics Infrastructure Facility of DBT at MANIT Bhopal.

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  1. Department of Bioinformatics, Computer Applications and Mathematics, Maulana Azad National Institute of Technology, Bhopal, MP, 462003, India

    Ashok Kumar Dwivedi

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  1. Ashok Kumar Dwivedi
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Correspondence to Ashok Kumar Dwivedi.

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Dwivedi, A.K. Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Comput & Applic 29, 1545–1554 (2018). https://doi.org/10.1007/s00521-016-2701-1

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