An efficient hybrid methodology for detection of cancer-causing gene using CSC for micro array data
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ORIGINAL RESEARCH
An efficient hybrid methodology for detection of cancer‑causing gene using CSC for micro array data A. Sampathkumar1 · Ravi Rastogi2 · Srinivas Arukonda3 · Achyut Shankar4 · Sandeep Kautish5 · M. Sivaram6 Received: 1 November 2019 / Accepted: 17 January 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Cancer is deadly diseases still exist with a lot of subtypes which makes lot of challenges in a biomedical research. The data available of gene expression with relevant gene selection with eliminating redundant genes is challenging for role of classifiers. The availability of multiple scopes of gene expression data is curse, the selection of gene is play vital role for refining gene expression data classification performance. The major role of this article is to derive a heuristic approach to pick the highly relevant genes in gene expression data for the cancer therapy. This article demonstrates a modified bio-inspired algorithm namely cuckoo search with crossover (CSC) for choosing genes from technology of micro array that are able to classify numerous cancer sub-types with extraordinary accuracy. The experiment results are done with five benchmark cancer gene expression datasets. The results depict that CSC is outperforms than CS and other well-known approaches. It returns 99% accuracy in a classification for the dataset namely prostate, lung and lymphoma for top 200 genes. Leukemia and colon dataset CSC is 96.98% and 98.54% respectively. Keywords Cancer diagnosis · Cuckoo search · Gene expression data · Genetic algorithm · Classification
* M. Sivaram [email protected] A. Sampathkumar [email protected] Ravi Rastogi [email protected] Srinivas Arukonda [email protected] Achyut Shankar [email protected] Sandeep Kautish [email protected] 1
School of Computing Science and Engineering, VIT Bhopal University, Bhopal, India
2
The Faculty of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia
3
Department of Computer Science and Engineering, KCC Institute of Technology and Management, Greater Noida, India
4
Department of CSE, Amity University, Noida, India
5
Dean‑Academics, LBEF Campus, Kathmandu, Nepal
6
Department of Computer Networking, Lebanese French University, Kurdistan Region, Erbil, Iraq
1 Introduction Past one decade, the microarray is a technology used in pharmacological treatment of diseases such as oral lesions and in cancer research. Scientists can evaluate the expression levels for numerous numbers of genes in a single experiment with the help of microarray technology in a efficient manner (Altman 1995) Basically, microarray analysis of data is a multi-step process: ribo nucleic acid (RNA) is take out from the sample using a column or solvent like phenol–chloroform that create contrary transcription of the messenger ribo nucleic acid (mRNA), complementary DNA strand (cDNA) is done; the categorized cDNAs from both the sections are placed in the DNA microarray so that complementar
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