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Survey on different Methods for Classifying Gene Expression using Microarray Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 150 - Number 1 |
| Year of Publication: 2016 |
| Authors: Emad Mohamed Mashhour, Enas M.F.El Houby, Khaled Tawfik Wassif, Akram I. Salah |
10.5120/ijca2016911441
|
Emad Mohamed Mashhour, Enas M.F.El Houby, Khaled Tawfik Wassif, Akram I. Salah . Survey on different Methods for Classifying Gene Expression using Microarray Approach. International Journal of Computer Applications. 150, 1 ( Sep 2016), 12-21. DOI=10.5120/ijca2016911441
Abstract
The recognizing and pre-detecting process of genetic mutation becomes an important issue for research. There are various techniques that may help in detecting diseases, cancer and tumors. Microarray is considered as type of representation for gene expression that may help in detection process. M microarrays are considered as a representation for samples that contains gene expression. These gene expressions are used in analyzing samples that may be normal or affected, and help in diagnosis. To utilize the benefit of microarrays, machine learning algorithms and gene selection methods must be used to facilitate processing on microarrays and to overcome some challenges that may face microarrays. From these challenges that may face Microarrays; high dimensional data problem which is considered an important challenges that rise in different datasets. It suffers from redundant, irrelevant and noisy data. Solving this problem requires a method that simplifies this representation. Feature selection process can be a solution that may solve this important problem, and to reduce the number of features to be used in clustering and classification. The problem can be defined as a selection of a small subset of genes from a set of gene expression data, recorded on DNA micro-arrays for classification. This survey observes some various techniques of classification, and gene selection methods such as filters and wrappers methods. To determine the suitable hybrid method or the powerful model that combine different techniques for detecting new or difficult mutated disease. And also introduces different emerging swarm intelligence techniques that prove its challenging ability in feature selection and classification in microarrays. These emerged techniques proved that there are upcoming approaches that can be used in detecting cancer. Swarm intelligence techniques proved that it can be hybridized with any mathematical or statistical techniques to gain better results.
References
- Karmen Stankov,” Bioinformatics tools for cancer geneticists”, Arch Oncol, 2005; 13(2):69-75.
- P.K.Vaishali & Dr. A.vinayababu,” Application of Microarray Technology and Soft computing in Cancer Biology: A Review”, 2011, International Journal of Biometrics and Bioinformatics (IJBB), Volume (5): Issue (4): 225
- Martin Dufva,”Introduction to Microarray Technology”, “DNA Microarrays for Biomedical Research: Methods and Protocols”, vol. 529, 2009
- Yvan Saeys, I ˜naki Inza and Pedro Larra˜naga,” A review of feature selection techniques in bioinformatics”, August 24, 2007, Vol. 23 no. 19 2007, pages 2507–2517
- Adi L. Tarca, Roberto Romero, Sorin Draghici,” Analysis of microarray experiments of gene expression profiling”, American Journal of Obstetrics and Gynecology (2006) 195, 373–88
- Erick Suárez, Ana Burguete, Geoffrey McLachlan,” review article Microarray Data Analysis for Differential Expression: a Tutorial”, PRHSJ Vol. 28 No. 2, June, 2009
- Rainer Breitling,” Biological microarray interpretation: the rules of engagement”, 2006 Jul; 1759(7):319-27. Epub 2006 Jul 13.
- Liang Lan, Slobodan Vucetic,” Improving accuracy of microarray classification by a simple multi-task feature selection filter”,Int. J. Data Mining and Bioinformatics, Vol. 5, No. 2, 2011
- Dorina Kabakchieva,” Predicting Student Performance by Using Data Mining Methods for Classification”, 2013, CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 13, No 1
- K Raja Sekhar, V Srinivasa Kalyan, B Phanindra Kumar,” Training of Artificial Neural Networks in Data Mining”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-3, Issue-2, and July 2013
- V. Anuja Kumari, R.Chitra ,” Classification Of Diabetes Disease Using Support Vector Machine”, International Journal of Engineering Research and Applications (IJERA) ,Vol. 3, Issue 2, March -April 2013, pp.1797-1801
- Xin-She Yang and Xingshi He, “Swarm Intelligence and Evolutionary Computation: Overview and Analysis”, 2015, Recent Advances in Swarm Intelligence and Evolutionary Computation, Studies in Computational Intelligence
- Ms. T.Hashni , Ms .T.Amudha,” Relative Study of CGS with ACO and BCO Swarm Intelligence Techniques”,2012, Int.J.Computer Technology & Applications, Vol 3 (5), 1775-1781
- Adis ALIHODZIC Milan TUBA, “Framework for Bat Algorithm Optimization Metaheuristic”, Recent Researches in Medicine, Biology and Bioscience, Bioscience 4th International Conference on Bioscience and Bioinformatics (ICBB '13), Chania, Crete Island, Greece; 08/2013
- M. Dorigo, V. Maniezzo, et A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics--Part B , volume 26, num. 1, pages 29-41, 1996.
- Poonam Sehrawat, Harish Rohil, “Taxonomy of Swarm Optimization”, Volume 3, Issue 8, August 2013, International Journal of Advanced Research in Computer Science and Software Engineering
- Chuanliang Chen, ChuanXu and RongfangBie, “Artificial Immune Recognition System for DNA Microarray Data Analysis”, Natural Computation, 2008, ICNC '08. Fourth International Conference (Volume: 6), Page(s): 633 – 637
- Nazario D. Ramirez-Beltran, Joan M. Castro, Harry Rodriguez,” A Projection Algorithm to Detect Cancer Using Microarray”, IAES International Journal of Artificial Intelligence (IJ-AI), Vol. 1, No. 2, June 2012, pp. 91~102
- A. Bharathi, Dr.A.M.Natarajan, “Cancer Classification of Bioinformatics data using ANOVA”, June, 2010, International Journal of Computer Theory and Engineering, Vol. 2, No. 3, 1793-8201
- Xiaosheng Wang and Osamu Gotoh,” A Robust Gene Selection Method for Microarray-based Cancer Classification”, 2010, Cancer Informatics, pages15–30
- Katerina N. Karayianni, George M. Spyrou and Konstantina S. Nikita,” Clustering Microarray Data using Fuzzy Clustering with Viewpoints ” ,Proceedings of the 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), November 2012
- Sudip Mandal and Indrojit Banerjee,” Cancer Classification Using Neural Network”, International Journal of Emerging Engineering Research and Technology, Volume 3, Issue 7, July 2015, PP 172-178
- Hualong Yu, n, Jun Ni, Jing Zhao,” ACOSampling: An ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data”, 2013, Neurocomputing, pages: 309-318
- Mustafa Serter Uzer, Nihat Yilmaz, Onur Inan,”Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification”, 2013.
- Hesham Arafat, Rasheed M.Elawady, Sherif Barakat and Nora M.Elrashidy,” Using Rough Set and Ant Colony optimization In Feature Selection”, February 2013,volume 2,Issue 1.
- AHMED MAJID TAHA,ALICIA Y.C. TANG,” BAT ALGORITHM FOR ROUGH SET ATTRIBUTE REDUCTION”, Journal of Theoretical and Applied Information Technology,2013,vol 51,No1, ISSN: 1992-8645
- P. Ganesh Kumar, S. Arul Antran Vijay, D.Devaraj,” A Hybrid Colony Fuzzy System for Analyzing Diabetes Microarray Data”, IEEE, 2013, Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
- N. Suguna and Dr. K. Thanushkodi,”A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization”, 2010, VOLUME 2, ISSUE 6
- K.Sathishkumar, Dr.V.Thiagarasu, M.Ramalingam,” AN EFFICIENT ARTIFICIAL BEE COLONY AND FUZZY C MEANS BASED CLUSTERING GENE EXPRESSION DATA”,2013,vol1,issue 5, International Journal of Innovative Research in Computer and Communication Engineering.
- Rodrigo Yuji Mizobe Nakamura, Luı´s Augusto Martins Pereira, Douglas Rodrigues, Kelton Augusto Pontara Costa, Joa˜o Paulo Papa and Xin-She Yang,” Binary Bat Algorithm for Feature Selection”,2013, Swarm Intelligence and Bio-Inspired Computation.
- Hala Alshamlan, Ghada Badr and Yousef Alohali,” mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling”, 2015, Volume 2015, Article ID 604910, 15 pages, BioMed Research International.
- Kun-Huang Chen, Kung-JengWang, Min-Lung Tsai, Kung-Min Wang, Angelia Melani Adrian, Wei-Chung Cheng, Tzu-Sen Yang, Nai-Chia Teng, Kuo-Pin Tan and Ku-Shang Chang, ”Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm”,2014,Chen et al. BMC Bioinformatics 2014, 15:49
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