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Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data

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Abstract

Cancer is a severe condition of uncontrolled cell division that results in a tumor formation that spreads to other tissues of the body. Therefore, the development of new medication and treatment methods for this is in demand. Classification of microarray data plays a vital role in handling such situations. The relevant gene selection is an important step for the classification of microarray data. This work presents gene encoder, an unsupervised two-stage feature selection technique for the cancer samples’ classification. The first stage aggregates three filter methods, namely principal component analysis, correlation, and spectral-based feature selection techniques. Next, the genetic algorithm is used, which evaluates the chromosome utilizing the autoencoder-based clustering. The resultant feature subset is used for the classification task. Three classifiers, namely support vector machine, k-nearest neighbors, and random forest, are used in this work to avoid the dependency on any one classifier. Six benchmark gene expression datasets are used for the performance evaluation, and a comparison is made with four state-of-the-art related algorithms. Three sets of experiments are carried out to evaluate the proposed method. These experiments are for the evaluation of the selected features based on sample-based clustering, adjusting optimal parameters, and for selecting better performing classifier. The comparison is based on accuracy, recall, false positive rate, precision, F-measure, and entropy. The obtained results suggest better performance of the current proposal.

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Acknowledgements

The authors are indebted to the editor and anonymous reviewers for their helpful comments and suggestions. The authors wish to thank GIK Institute for providing research facilities. This work was sponsored by the GIK Institute graduate research fund under GA-F scheme.

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Authors and Affiliations

  1. The Machine Intelligence Research Group (MInG), Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan

    Uzma & Zahid Halim

  2. College of Technological Innovation at Zayed University, Abu Dhabi, UAE

    Feras Al-Obeidat, Abdallah Tubaishat & Babar Shah

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  1. Uzma
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  5. Zahid Halim
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Uzma, Al-Obeidat, F., Tubaishat, A. et al. Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data. Neural Comput & Applic 34, 8309–8331 (2022). https://doi.org/10.1007/s00521-020-05101-4

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