Article: Optimised feature selection and entropy-based graph classification of gene expression data Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2020 Vol.12 No.4 pp.357 - 374 Abstract: Gene expression (GE) profiles expansively revised to disclose intuition into the multifariousness of cancer furthermore to discover concealed information which provides biological knowledge for the classification of cancer. Precise cancer classification straightly through original GE profiles stays challenging on account of the intrinsic high-dimension feature along with the small magnitude of the data samples. Therefore, choosing high discriminative genes as of the GE data have turn into progressively fascinating in the bioinformatics field. This given paper gives a technique for the GE data classification utilising entropy-based graph classifier. Initially, the proposed technique evaluate the GE data's signal to noise ratio (SNR) values, additionally, selects the relevant features using krill herd (KH) optimization process. The truth is that not all features are helpful for classification, and some redundant together with the irrelevant features might even serve as outlier. To dispose the outliers, feature reduction is done with the assist of Euclidean distance. Classification is made utilising entropy-based graph classifier. The proposed process' effectiveness contrasted with the existing method concerning classifications is established from the experimental outcome. Inderscience Publishers - linking academia, business and industry through research

Title: Optimised feature selection and entropy-based graph classification of gene expression data

Authors: Audu Musa Mabu; Rajesh Prasad; Raghav Yadav

Addresses: Department of Computer Science & IT, Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad, U.P. India ' School of I.T & Computing, American University of Nigeria, Yola, Nigeria ' Department of Computer Science & IT, Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad ,U.P India

Abstract: Gene expression (GE) profiles expansively revised to disclose intuition into the multifariousness of cancer furthermore to discover concealed information which provides biological knowledge for the classification of cancer. Precise cancer classification straightly through original GE profiles stays challenging on account of the intrinsic high-dimension feature along with the small magnitude of the data samples. Therefore, choosing high discriminative genes as of the GE data have turn into progressively fascinating in the bioinformatics field. This given paper gives a technique for the GE data classification utilising entropy-based graph classifier. Initially, the proposed technique evaluate the GE data's signal to noise ratio (SNR) values, additionally, selects the relevant features using krill herd (KH) optimization process. The truth is that not all features are helpful for classification, and some redundant together with the irrelevant features might even serve as outlier. To dispose the outliers, feature reduction is done with the assist of Euclidean distance. Classification is made utilising entropy-based graph classifier. The proposed process' effectiveness contrasted with the existing method concerning classifications is established from the experimental outcome.

Keywords: gene expression profile; bioinformatics; entropy; graph classifier; krill herd optimisation; signal to noise ratio; SNR.

DOI: 10.1504/IJMEI.2020.108239

International Journal of Medical Engineering and Informatics, 2020 Vol.12 No.4, pp.357 - 374

Received: 16 Apr 2018
Accepted: 16 Jun 2018
Published online: 07 Jul 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Optimised feature selection and entropy-based graph classification of gene expression data

Keep up-to-date