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

Author First name, Last name, Institution

Uzma, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology
Feras Al-Obeidat, Zayed University
Abdallah Tubaishat, Zayed University
Babar Shah, Zayed UniversityFollow
Zahid Halim, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology

Document Type

Article

Source of Publication

Neural Computing and Applications

Publication Date

1-1-2020

Abstract

© 2020, Springer-Verlag London Ltd., part of Springer Nature. 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.

DOI Link

10.1007/s00521-020-05101-4

ISSN

0941-0643

Publisher

Springer

Last Page

23

Disciplines

Computer Sciences

Keywords

Clustering, Deep learning, Gene expression, Genetic algorithm, Unsupervised learning

Scopus ID

85086476667

Recommended Citation

Uzma; Al-Obeidat, Feras; Tubaishat, Abdallah; Shah, Babar; and Halim, Zahid, "Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data" (2020). All Works. 1768.
https://zuscholars.zu.ac.ae/works/1768

Indexed in Scopus

yes

Open Access

no