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1 1393/1/31: 1393/4/12: 1393 "# /292 / " # $ % & $ '$ 2 1 #&( ). " # " $%&' ( ) * +, -.# / $ ) > =; < #) 8 # #" 8" DNA (#5." " # : " ( (, $%&' H > / 5 (G (.# F3" (AE ( C D)* (>" ; -. A@ ( " SVM) &' # ;" (Stepwise linear discriminate analysis) SWLDA (AE ( " (.5 I (Leave one out) LOO -.# A@ (K-Nearest Neighbours KNN) >) /64 K. (Support vector machine.e /@ ".. A.K. # 4)4 ; A #E $J 4 G 231 (.# &' # ;".I -.# O Specificity. Sensitivity IL 6" M4 $3N +4E &' # ;" -.# :.Q K /@ L# 82 /64 Specificity. L# 84 P Sensitivity I>4 $3N +4E A@ $KS. TJ 4 (DR" / ( +, AE ( E I # <. E $ 6" SWLDA -.# #" # :.E$" & # AE ( (Y' H)1. )E # (KH)U V $KW' 2X>" E $" AO K-nearest neighbours Support vector machine (AE ( $%&' #5." : Stepwise linear discriminate analysis * )+ $+,+ -./0 12 ()) )*#$ %&' ".ZD* ( (O" $" : :(292) 32 \1393 O@L $;6' A&4 D[".3 %,(4+ * ) +8>% 8=8 "< " (Histological grade Lymph node status "88, A *? B &E3 )E? ;.*D 8 A$8 )+B C E "88, F8 DNA 3 * <, D - B 8= 8 " #$% & '( +,-. /% " # )* #0 12 * " + ;,8 8,.(1-2) *+ - "3 " mehri@med.mui.ac.ir " " #$% & ' ( -1 " " #$% & ' ( +, *- - * -2 : 1393 " # /292 /

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3 " % & % '() * +,- $""# $" (SWLDA) & ;,D (Linear discriminant analysis) LDA s \.# 3 D* *? ;, % S$ "+0g t8, +>8>\ + +,c +(Q *= t8, ) * Q +0g +(Q *= t8,.* 3+ XY ;E, j*c PE $ ;,D +0 += c ^CY "% P M % &0 ;E )D % D* *? ;.*8+ X8T *? D* *? J < * j$ MAP ;Y, 1c # u$ (Maximum a posteriori) +0g S$ "+0g + 12 t8, t, )? l8 "l,, ; s".(6-) 8+ T " t & SWLDA D* *? *? Cc jo P # LDA D - +0g XY % -% < J 1* +0g x" )w.(8-9) * + K +0g ) 8+ $0 $] *K. 8+ $] 1* )* D 3 +0 * J )1* **= " * ; +"+0g eq2 % j0 j0 +, )* $ ;. + )* #" D* ;K,.% "+0g *K, P 1* D* $] +0g x" +, )* )* D 3 /iq K *.*+ ;>) Intensity % * M, " (& & 1 " Intensity +* " "I )1 C2.* T I "." I.&$ )n, * n, +"I ; lm.**?, I n, 2. +"3 )* "I *K, )a,, ; ** XY )*.#$." I 5000 *2 + 8 "I ; J " +>" & )*K C2 * T (Disease outcome) O O 0/3. +>" I "I ** p-2 )*.** 0 eq2 JB-, )8>E + Er " +M e*" ^CY? * \ "8 c8o 0 JB-, )+M e*" = ;.# *?.# *$ D0 +C * J "D*.*8+?, #% * #% D c8o J #% "< +83 c8o 8 c )D 8Q a\. 8+ D - D* *? "%, S$ )*? & )> c J "3 ; F, Er +=Q.(5) D )VW I 231 XY l% &."g% ; 0 JB-, "D* *?.* D - * / D " # /292 /

4 " :# j$ q% +*= #E2 c8o X = {X 1, X 2,..., X n } + u$? "a\ " y {-1,1} "8 "8 # +(Q z, ;$ )e*".* "8 B *2, * *? + 2 PB A(? ; 0.*? * T-L J )* q% +*= +(Q 8L.* + *= " "3 f(x) &, t, H < D* XY "+0g )1 1*=.# D*3 E I j0 j0 SWLDA 12 34" $ 5+0(.1./ NM Contig 553-RC NM Contig 5864-RC 152 NM Contig NM Contig 18-RC 105 Contig RC 18 AF NM Contig RC 21 NM SWLDA: Stepwise linear discriminate analysis +,- (<* +(/ 9: ; 6*.2 H:W.x + b = 0,f() = sing (W.x + b) + )H H ) 8,, (1) T-L ; CL$ s \ & - 2- m = 2/ ǁ W & 2 D* *? *"8Q,j? &8 O ) 2 uc ; M T-L J )"8 j, * * # F < D - # *? M Er.# P2 P g0b a] Minimize 1 2 W Subject to y W x b 1 i 1,, n (2) (SVM) 6* "D* *? % ; ky,.?$8 Q # % *? ;.* M l8# j ; )(Q (Q D* t,?$ ; a.* + ky? 2 # " *K, % " *0+? LDA * D* *? ;.*8+ D* w8 " N# D* *? J +(Q f w8.# 8=8 3 +(Q f +(Q t8, y8zq +(Q f "" SVM * + D - (Radial basis function) RBF +(Q % ;.(10-11) " # /292 / 32

5 " SVM )SWLDA D* *? D - *? F8 0 )KNN.** JB-, * F8 R )5, 3 1*= 44-33) F8 0 *? ; D - ( * - 8= PE # jb.# D*3 D* 0 +B X8C( Intensity? +Q * D0 ; "8 +B ) D0.* 0 eq2 T J " F8 "% & 6 1*=.# D*3 D - 8 "D* *? ;M )*" + 6 1*= 8" SWLDA KNN )SVM a,, "D* *? #8, +(Q P D - SVM.* + J & #LzQ *L 84. #2. m 0 JB-, *L 82 (KNN) (?5 ((=> K D J? ky J B, ; e*" <83 D +3 *+ )* " 3 "" ;,J & 18K 8 )"" ;,J & ;.(12-14) # 0D* P CL$ J D - "8 & +$ * K J.*3+ a?, +K, " *8,+ KNN )+$ <83 *8 KNN, 8+ Uc Er ; *&.* O +(Q f 0z, " 18K 8 )" ;,J & K < " A?T, ;. 8+ XY $ *c K D*O K ^CY *? * \ *? #TL #E2 R 2 1*= *.# D*3 K = 1,3,5 a *? *+ 2 1*= A ; K = 3 *+ 3 *c K.* 0 D - D* *? KNN C ( B $?(.2./ &&'#$% 0/666 " 0/8636 0/92 K K=1 0/89 0/8409 0/8182 K=3 0/56 0/8409 0/8052 K=5 KNN: K-Nearest Neighbours 2-. / K = 3 KNN C $" $EF./.3./, *+ KNN / K= /01-#". KNN: K-Nearest Neighbours 1393 " # /292 /

6 " SVM C $" $EF./.4./ 2-. / 6 2, *+ SVM: Support vector machine / SVM /01-#". SWLDA C $" $EF./.5./, *+ 3$4-. / / SWLDA: Stepwise linear discriminate analysis :0;< 608 SWLDA /01-#". " $ 5""# $" AJ $?(.6./ &&'#$% " 5 &&' &2-6 0/56 0/8409 0/8052 KNN 0/8182 0/8409 0/8312 SVM 0/89 0/23 0/532 SWLDA KNN: K-Nearest Neighbours; SVM: Support vector machine; SWLDA: Stepwise linear discriminate analysis 8+ D* 1*= =8,.* $0 < ) * / D0 0 * #2 )#TL & +(Q P SVM.# < ;M #LzQ $0 8L 8, ;M ;.%, )0 ;" # D 8 $0 8L (4) B" van t Veer 8 8 *? X8= ;M 3 + ; *3 # I 0 D - (8 8-65) 8 *L 83 #TL )]2."g% < +E2 N* ky #2 #8, +(Q P D - SVM *L 82 *2 #LzQ *L *? BCc + )A?T, ; e*" +8>% ^CY "D* ;*. 8 3 B 0 D - F8 3 B 0 )8 D* B $ + D 1 * 0 D - )* 8 )* 8 D* \ 1 5 P*2 ƒ * ; + j. 8 D % "D* *? =8, *? A?T, ; )J,8-8 8 KNN SVM )SWLDA D* " # /292 / 32

7 ".*" +.&$ D* *? q% K,.% +"D* *? +,8L PB D8" )* $0 D - 8 ; T O2 8 b " +0g +0&.*D :% D> $??T, #K C;*. 0+ &>m M-L +B&% j8cc # M R m 0 JB-,. 3 # q0 "< ; SWLDA < 8 > M B M, 1 1*= A D* *? ; # & ; I 231 = I 13 D - T O2 Er ; # D* #TL SWLDA < ;2 *" +.".# *? +0g *K, D -." 0z, t, +0*s% )Er ; References 1. Weigelt B, Peterse JL, van't Veer LJ. Breast cancer metastasis: markers and models. Nature Reviews 2005; 5: Lujambioa A, Calinc GA, Villanuevad A, Roperoa S, Sanchez-Cespedese M, Blancof D, et al. A microrna DNA methylation signature for human cancer metastasis. PNAS 2008; 105(36): Mehridehnavi A, Ziaei L. Minimal gene selection for classification and diagnosis prediction based on gene expression profile. Adv Biomed Res 2013; 2: van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415(681): Lotte F, Congedo M, Lecuyer A, Lamarche F, Arnaldi B. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng 200; 4(2): R1-R Huerta EB, Duval B, Hao JK. Selection for microarray data by a LDA-based genetic algorithm. Lecture Notes in Computer Science 2008; 5265: Sharmaa A, Paliwala KK. Cancer classification by gradient LDA technique using microarray gene expression data. Data and Knowledge Engineering 2008; 66(2): Krusienski DJ, Sellers EW, McFarland DJ, Vaughan TM, Wolpaw JR. Toward enhanced P300 speller performance. J Neurosci Methods 2008; 16(1): Nijboer F, Sellers EW, Mellinger J, Jordan MA, Matuz T, Furdea A, et al. A P300-based braincomputer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol 2008; 119(8): Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000; 16(10): Hernandez JCh, Duval B, Hao JK. SVM-based local search for gene selection and classification of microarray data. Bioinformatics Research and Development Communications in Computer and Information Science 2008; 13: Parry RM, Jones W, Stokes TH, Phan JH, Moffitt RA, Fang H, et al. k-nearest neighbor models for microarray gene expression analysis and clinical outcome prediction. Pharmacogenomics J 2010; 10(4): Jiangsheng Y. Method of k-nearest neighbors. Beijing, China: Institute of Computational Linguistics Peking University; Krusienski DJ, Sellers EW, Cabestaing F, Bayoudh S, McFarland DJ, Vaughan TM, et al. A comparison of classification techniques for the P300 Speller. J Neural Eng 2006; 3(4): " # /292 /

8 Journal of Isfahan Medical School Received: Vol. 32, No. 292, 1 st Week, September 2014 Accepted: Abstract Comparison of Different Classifiers for Prediction of Breast Cancer Metastasis in Microarray Analysis Zahra Amini MSc 1, Alireza Mehridehnavi PhD 2 Original Article Background: In this research, we investigated the performance of some different classifiers for prediction of metastasis in breast cancer. Methods: We used the DNA microarrays of primary breast tumors of 8 young patients. Among these patients, 34 had developed distant metastases within 5 years (poor prognosis group) and 44 formed good prognosis group. For analysis, we applied three different classifiers including support vector machine (SVM), stepwise linear discriminant analysis (SWLDA) and K-nearest neighbors (KNN) classifier. Each of these classifiers used 231 selected genes as an input feature vector and their performances were estimated via using leave one out (LOO) method to classify patients into two groups namely, good and poor prognosis. Findings: The best results were obtained by support vector machine with linear kernel. This classifier achieved a sensitivity and specificity of 84% and 82%, respectively, for metastasis prediction. Conclusion: Our findings provide a strategy to specify patients who would benefit from adjuvant therapy. Keywords: Microarrays, Prediction of breast cancer, Support vector machine (SVM), Stepwise linear discriminant analysis (SWLDA), k-nearest neighbors (KNN) classifiers Citation: Amini Z, Mehridehnavi A. Comparison of Different Classifiers for Prediction of Breast Cancer Metastasis in Microarray Analysis. J Isfahan Med Sch 2014; 32(292): PhD Student, Department of Bioelectric and Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran 2- Associate Professor, Department of Bioelectric and Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran Corresponding Author: Alireza Mehridehnavi PhD, mehri@med.mui.ac.ir " # /292 / 32