Type of Study: Research |
Subject: Genetis Published: 2016/01/5
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References :
Sheikhpour R. Mohiti Ardekani J. The effect of progesterone on p53 protein in T47D in cell line. Urmia Med J 2014; 25(10): 954-60.
Parsa N. Environmental Factors, Genes and Human Cancers. Sci Cultivation J 2012; 2(1): 12-9.
Sheikhpour R, Ghasemi N, Yaghmaei P, Mohiti J. Immunohistochemical assessment of p53 protein and its correlation with clinicopathological parameters in breast cancer patients. Indian J Sci Technol 2014; 7(4): 472-9.
Toloie Ashlagi A, Mohsen Taheri S. Designing an expert system for suggesting the blood cancer treatment. J Health Admin 2010; 13(40): 41-50. [Article in Farsi]
Zali H, Rezaei Tavirani M, Salimian J, Aolad GR, Basataminejad S. Gene expression networks to analysis DNA microarray data. Scientific Journal of Ilam University of Medical Sciences 2012; 20(4): 138-50. [Article in Farsi]
Getz G, Levine E, Domany E. Coupled two-way cl ustering analysis of gene microarray data. Proc Natl Acad Sci USA 2000; 97(22): 12079-84.
Hoyle DC, Rattray M, Jupp R, Brass A. Making sense of microarray data distributions. Bioinformatics 2002; 18(4): 576-84.
Kerr MK, Martin M, Churchill GA. Analysis of variance for gene expression microarray data. J Comput Biol 2002; 7(6): 819-37.
Long AD, Mangalam HJ, Chan BY, Tolleri L, Hatfield G, Baldi P. Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12. J Biol Chem 2001; 276(23): 19937-44.
colorectal cancer screening. Anticancer Res 2014; 34(1): 169-74.
Vassella E, Galván JA, Zlobec I. Tissue microarray technology for molecular applications: investigation of cross-contamination between tissue samples obtained from the same punching device. Microarrays 2015; 4(2): 188-95.
Kohbalan M, Mohd SM, Safaai D. A review on missing value imputation algorithms for microarray gene expression data. Current Bioinformatics 2014; 9(1): 18-22.
Duggan DJ, Bittner M, Chen Y, Meltzer P, Trent JM. Expression profiling using cDNA microarrays. Nat Genet 1999; 21 (1 Suppl): 10-4.
Molaeezadeh SF, Moradi MH. Selected genes containing microarray information using mutual
information and genetic algorithm. 13th Conf Med Eng; 2006; 1-5.
Wang Z. Neuro-fuzzy modeling for microarray cancer gene expression data. USA: Oxford University Computing Laboratory; 2005. p. 241-6.
Yu L, Liu H. Redundancy based feature selection for microarray data. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM 2004; 737-42.
Chen AH, Lin EJ. The prediction of cancer classification using a novel multi-task support vector sample learning technique. AISS: Adv Inform Sci Serv Sci 2011; 3(3): 92-9.
Cai H, Ruan P, Ng M, Akutsu T. Feature weight estimation for gene selection: a local hyperlinear learning approach. BMC Bioinformatics 2014; 15(1), 70.
Zhang H, Wang H, Dai Z, Chen M.S., Yuan Z. Improving accuracy for cancer classification with a new algorithm for genes selection. BMC Bioinformatics 2012; 13(1), 298.
Sci J Iran Blood Transfus Organ 2016; 12(4): 347-357
Diagnosis of acute myeloid and lymphoblastic leukemia using gene selection of microarray data and data mining algorithm
Sheikhpour R.1, Aghasaram M.1, Sheikhpour R.2,3
1School of Electrical & Computer Engineering, Yazd University, Yazd, Iran 2School of Medicine, Islamic Azad University, Yazd Branch, Yazd, Iran 3Hematology & Oncology Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Abstract Background and Objectives
Microarray technology represents the expression of thousands of genes simultaneously. Microarray analysis may not be possible without statistical analysis and artificial intelligence methods. The aim of this paper is to diagnose acute leukemia using microarray data and data mining algorithms.
Materials and Methods
The expression of 7129 genes of 72 patients with leukemia was used in this study. Then, by the selection of important genes based on correlation coefficient, information gain, gain ratio and fisher score criteria and by the use of linear discriminat, support vector machine, k nearest neighbor, naïve Bayes, Bayes net, nearest mean, logistic regression, multilayer perceptron neural network and J48 decision tree methods on the selected genes, acute myeloid and lymphoblastic leukemia were attemted to be diagnosed.
Results
The methods of nearest mean, support vector machine, k nearest neighbor, naïve Bayes, and multilayer perceptron neural network are able to detect acute myeloid and lymphoblastic leukemia using 39 selected genes by the gain ratio with 100 percent accuracy. Moreover, support vector machine method using 87 selected genes by information gain and support vector machine method using 133 selected genes by information gain are able to detect acute myeloid and lymphoblastic leukemia with 100 percent accuracy.
Conclusions
The results of this study showed that gene selection and data mining algorithm are able to diagnose leukemia with high accuracy. Therefore, appropriate decisions can be made using these methods about the how of the diagnosis and treatment of patients.
Correspondence: Sheikhpour R., PhD of Biochemistry. School of Medicine, Islamic Azad University, Yazd Branch and Hematology & Oncology Research Center, Shahid Sadoughi University of Medical Sciences.
P.O.Box: 89156-56965, Yazd, Iran. Tel: (+9835) 36282884; Fax: (+9835) 36282884
E-mail: robab.sheikhpour@iauyazd.ac.ir
Sheikhpour R, Aghaseram M, Sheikhpour R. Diagnosis of acute myeloid and lymphoblastic leukemia using gene selection of microarray data and data mining algorithm. Sci J Iran Blood Transfus Organ 2016; 12 (4) :347-357 URL: http://bloodjournal.ir/article-1-930-en.html