An efficient hybrid methodology for detection of cancer-causing gene using CSC for micro array data

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.

[1]  E. Boerwinkle,et al.  Feature (gene) selection in gene expression-based tumor classification. , 2001, Molecular genetics and metabolism.

[2]  Reza Modarres,et al.  Detecting discordance enrichment among a series of two-sample genome-wide expression data sets , 2016, BMC Genomics.

[3]  David M. Umbach,et al.  A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data , 2017, BMC Genomics.

[4]  Pradipta Maji,et al.  Mutual Information-Based Supervised Attribute Clustering for Microarray Sample Classification , 2012, IEEE Transactions on Knowledge and Data Engineering.

[5]  A. Sampathkumar,et al.  Gene Selection Using Parallel Lion Optimization Method in Microarray Data for Cancer Classification , 2019, J. Medical Imaging Health Informatics.

[6]  Xiaosheng Wang,et al.  Accurate molecular classification of cancer using simple rules , 2009, BMC Medical Genomics.

[7]  T. Jarin,et al.  Numerical analysis of circularly polarized modes in coreless photonic crystal fiber , 2019, Results in Physics.

[8]  Zhonglu Ren,et al.  Identifying molecular subtypes in human colon cancer using gene expression and DNA methylation microarray data , 2015, International journal of oncology.

[9]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[10]  P. Madhavan,et al.  Numerical investigation of temperature dependent water infiltrated D-shaped dual core photonic crystal fiber (D-DC-PCF) for sensing applications , 2019, Results in Physics.

[11]  L. K. Buehler,et al.  Normalizing DNA microarray data. , 2002, Current issues in molecular biology.

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  A. Frary,et al.  Genome-wide SNP discovery and QTL mapping for fruit quality traits in inbred backcross lines (IBLs) of solanum pimpinellifolium using genotyping by sequencing , 2016, BMC Genomics.

[14]  R. Simon,et al.  Analysis of DNA microarray expression data. , 2009, Best practice & research. Clinical haematology.

[15]  Ujjwal Maulik,et al.  Fuzzy Preference Based Feature Selection and Semisupervised SVM for Cancer Classification , 2014, IEEE Transactions on NanoBioscience.

[16]  F. Azuaje,et al.  Multiple SVM-RFE for gene selection in cancer classification with expression data , 2005, IEEE Transactions on NanoBioscience.

[17]  Qian Zhang,et al.  A Hybrid Particle Swarm Optimization-Cuckoo Search Algorithm and Its Engineering Applications , 2019, Mathematical Problems in Engineering.

[18]  Geoffrey J. McLachlan,et al.  Analyzing Microarray Gene Expression Data , 2004 .

[19]  Zhengwei Chang,et al.  New principle for busbar protection based on the Euclidean distance algorithm , 2019, PloS one.

[20]  Ali Najafi,et al.  A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata , 2017 .

[21]  Sung-Bae Cho,et al.  Machine Learning in DNA Microarray Analysis for Cancer Classification , 2003, APBC.

[22]  I. Chung,et al.  Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index , 2011, PloS one.

[23]  Lei Liu,et al.  Ensemble gene selection for cancer classification , 2010, Pattern Recognit..

[24]  Parham Moradi,et al.  Gene selection for microarray data classification using a novel ant colony optimization , 2015, Neurocomputing.