Lymphoma Cancer Classification Using Genetic Programming with SNR Features

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1 Lymphoma Cancer Classfcaton Usng Genetc Programmng wth SNR Features Jn-Hyuk Hong and Sung-Bae Cho Dept. of Computer Scence, Yonse Unversty, 134 Shnchon-dong, Sudaemoon-ku, Seoul , Korea Abstract. Lymphoma cancer classfcaton wth DNA mcroarray data s one of mportant problems n bonformatcs. Many machne learnng technques have been appled to the problem and produced valuable results. However the medcal feld requres not only a hgh-accuracy classfer, but also the n-depth analyss and understandng of classfcaton rules obtaned. Snce gene expresson data have thousands of features, t s nearly mpossble to represent and understand ther complex relatonshps drectly. In ths paper, we adopt the SNR (Sgnal-to-Nose Rato) feature selecton to reduce the dmensonalty of the data, and then use genetc programmng to generate cancer classfcaton rules wth the features. In the expermental results on Lymphoma cancer dataset, the proposed method yelded 96.6% test accuracy n average, and an excellent arthmetc classfcaton rule set that classfes all the samples correctly s dscovered by the proposed method. 1 Introducton Accurate decson and dagnoss of the cancer are very mportant n the feld of medcne whle they are very dffcult [1,2]. Exact classfcaton of cancers makes t possble to treat a patent wth proper treatments and helpful medcnes so as to save the patent s lfe. Over several centures, varous cancer classfcaton technques are developed, but most of them are based on the clncal analyss of morphologcal symptoms for the cancer. Wth these methods, even a medcal expert causes many errors and msunderstandngs, because n many cases dfferent cancers show some smlar symptoms. In order to overcome these restrctons, classfcaton technques usng human s gene nformaton have been actvely nvestgated, and many good results have been reported recently [1,2,3] Gene nformaton, usually called gene expresson data, s collected by the DNA mcroarray technque wth keen nterests. The gene expresson data nclude lots of gene nformaton on lvng thngs [2]. Usually, the gene expresson data provde useful nformaton for the classfcaton of dfferent knds of cancers. Snce the orgnal format of the data s an array of smple numbers, t s not easy to analyze them drectly and to dscover useful classfcaton rules of the cancer. Several methods for t have been studed for several years n artfcal ntellgence [2,3]. Table 1 shows related works on the classfcaton of lymphoma cancer usng DNA mcroarray data. M. Kejzer et al. (Eds.): EuroGP 2004, LNCS 3003, pp , Sprnger-Verlag Berln Hedelberg 2004

2 Lymphoma Cancer Classfcaton Usng Genetc Programmng wth SNR Features 79 Table 1. Related works Author Data Method Accuracy Feature selecton Classfer (%) L et al. Genetc algorthm Knn 84.6 Nearest neghbor 95.0 Dudot et al. Lymphoma 95.0 Nguyen et al. The rato of between-groups to wthn-groups sum of squares PCA Dagonal lnear dscrmnant analyss Logstc dscrmnant 98.1 Boost CART 97.6 It s not easy to obtan a good classfcaton performance wth gene expresson data, because the data consst of a few samples wth a large number of varables. Nevertheless dverse technologes of artfcal ntellgence have been appled to classfy the cancer and shown a superor performance of the classfcaton. However, many conventonal approaches such as the neural network and SVMs are not easy to be drectly nterpreted. In medcal area dscovered rules should be understandable for people to get a confdence [4]. In ths paper, we propose a classfcaton rule generaton method whch s composed of the SNR feature selecton and genetc programmng so as to obtan precse and comprehensble classfcaton rules, whch also produces an outstandng performance from hgh dmensonal gene expresson data by desgnng the rule wth arthmetc operatons. 2 Backgrounds 2.1 DNA Mcroarray An organsm bascally has thousands of genes, RNA and proten. Tradtonal molecular bology has only consdered a sngle gene, so the obtaned nformaton s very lmted to be appled varous problems. DNA mcroarray has been developed recently, and t successfully deals wth the problem. It acqures gene nformaton n terms of mcroscopc unts, and the revelaton phase of a total chromosome on a chp s observed by ths technque. That s, DNA mcroarray technque makes t possble to analyze and observe for a complex organsm n detal [1,2,3]. DNA mcroarray fxes cdna of hgh densty on a sold substrate whch s not permeated wth a soluton, whle t attaches thousands of DNA and proten at regular ntervals on the sold substrate and combnes wth the target materals. The phase of the combnaton can be observed on the chp. Each cell on the array s syntheszed wth two gene materals collected by dfferent envronments and dfferent fluorescent dyes mxed (green-fluorescent dye Cy3 and red-fluorescent dye Cy5 n equal quanttes). After the hybrdzaton of these samples wth the arrayed DNA probes, the sldes are maged by a scanner that makes the fluorescence measurement for each dye. The overall procedure of DNA mcroarray technology s as shown n Fg.1 and the log rato between the two ntenstes of each dye s used as the gene expresson as follows.

3 80 J.-H. Hong and S.-B. Cho DNA clones test reference laser1 exctaton laser2 PCR amplfcaton, purfcaton reverse transcrpton label wth fluor dyes Cy 3 Cy 5 emsson robotc prntng hybrdze target to mcroarray computer analyss Fg. 1. Overvew of DNA mcroarray technology Int( Cy5) gene _ expresson = log 2 Int( Cy3) where Int(Cy5) and Int(Cy3) are the ntenstes of red and green colors. Snce at least hundreds of genes are put on the DNA mcroarray, we can nvestgate the genomewde nformaton n short tme. 2.2 Genetc Programmng Genetc programmng s devsed to desgn a program whch solves a problem automatcally wthout a user s explct programmng. It regards a program as a structure composed of functons and varables. The program usually has a tree structure to represent the ndvdual s nformaton [13]. Genetc programmng s one of evolutonary computaton technques lke the genetc algorthm. Basc operatons and characterstcs are smlar to those of the genetc algorthm, but they are dfferent n terms of the representaton. The soluton space of genetc programmng s very wde reachng to problems whch can be solved by a program wth functons and varables [10,11,14]. There are varous functons for genetc programmng such as arthmetc operatons, logcal operatons, and userdefned operatons. Recently, t has been appled to many problems such as optmzaton, the evoluton of assembly language program, evolvable hardware, the generaton of a vrtual character s behavors, etc [13]. 3 Classfcaton Rule Dscovery In ths paper, we propose a rule dscovery method as shown n Fg. 2. Frst, the SNR feature selecton reduces the dmensonalty. And then, genetc programmng fnds out good classfcaton rules wth the SNR features.

4 Lymphoma Cancer Classfcaton Usng Genetc Programmng wth SNR Features 81 Feature selecton wth SNR Classfcaton rule dscovery wth GP GP rule extractor Gene expresson data Fg. 2. The proposed method to classfy DNA mcroarray profles 3.1 Sgnal-to-Nose Rato Feature Selecton Snce not all the genes are assocated wth a specfc dsease, the feature selecton often called gene selecton s necessary to extract nformatve genes for the classfcaton of the dsease [3,15,16]. Moreover, feature selecton accelerates the speed of learnng a classfer and removes noses n the data. There are two major feature selecton approaches: flter and wrapper approaches. The former selects nformatve features (genes) regardless of classfers. It ndependently measures the mportance of features, and selects some for the classfcaton. On the other hand, the latter selects features together wth classfers. It s smultaneously done by the tranng of a classfer to produce the optmal combnaton of features and a classfer. Snce the flter approach s smple and fast enough to obtan hgh performance, we evaluated varous flter-based feature selecton methods [15]. Fnally sgnal-to-nose rato rankng method s adopted to select useful features. After measurng the sgnal to nose rato of genes, 30 genes are selected based on ther ranks. µ c1( g ) µ c0 ( g ) SN( g, C) = σ c1 ( g ) + σ c0 ( g ) µ 1 ( g) : the average of genes n class C µ 2 ( g) : the average of genes not n class C σ 1 ( g) : standard devaton of genes n class C σ ( g) : standard devaton of genes not n class C 2 Sgnal-to-nose rato measures how the sgnal from the defect compares to other background nose. In bonformatcs the sgnal represents useful nformaton conveyed by genes, and nose to anythng else on the genes. Hence a low rato mples that the gene s not worth for the class C whle a hgh rato means that the gene s rather related wth the class C.

5 82 J.-H. Hong and S.-B. Cho Table 2. Arthmetc operators used n ths paper Arthmetc operator Functon Descrpton + Addton Postve effect on class 1(Negatve effect on class 2) Subtracton Negatve effect on class 1(Postve effect on class 2) Multplcaton Multplcatve correlaton / Dvson Dvsve correlaton 3.2 Classfcaton Rule Extracton Conventonal rule dscovery usng genetc programmng has usually adopted frstorder logc [17] or IF-THEN structure as the rule, whle logc operatons AND, OR, Not and comparatve operatons (<, >, =) are frequently used as follows [4,12]. Rule1: Rule2 : IF(( A1 < 0.6) OR ( A3 > 0.3)) THEN IF(( A2 = 0.7) AND ( A1 > 0.7))THEN class1 class2 Although these rules are easy to be nterpreted, t has a lmtaton to represent more complex relatonshps among varables to get a hgh performance [12]. Mathematcal operatons have been also tred to construct a rule, but they are dffcult n the analyss. Moreover n some applcatons t s already known that they obtan lower accuracy than arthmetc operatons. In ths paper, arthmetc operatons are used to construct a more sophstcated rule leadng to hgh accuracy. A rule s desgned as a tree wth 30 SNR features and basc arthmetc operatons ( +, -,, / ). Although numercal value can be also consdered as a termnal, t s not used n ths experment. For the easy analyss of rules obtaned, the meanngs of arthmetc operatons for genes are defned n Table 2. The classfcaton rule s constructed as follows. As shown n Fg. 3, the value of the functon eval() represents whch class a sample belongs to. Postve value ndcates that the sample belongs to class 1, whle negatve value sgnfes that the sample s classfed nto class 2. IF eval( Indvdual) 0 THEN class1 ELSE class2 We have expermented wth three knds of rule representatons. Not all arthmetc operators are used as shown n Table 3, but the 2 nd and the 3 rd wthout and / operators are used to keep the smplcty of the rule. Weghts show that whch gene s more effectve for the classfcaton whle the values are from 0 to 1.0. Fg. 4 brefly shows the three rule representatons.

6 Lymphoma Cancer Classfcaton Usng Genetc Programmng wth SNR Features 83 Table 3. Rule representatons to be tested No + - / Weghtng Complexty 1 Use Use Use Use Not-use Hgh 2 Use Use Not-use Not-use Use Mddle 3 Use Use Not-use Not-use Not-use Low Fg. 3. Representaton of the proposed method and classfcaton rule È ) ) ) ) È) È) È) È) ) ) ) ) DUXOHUHSUHVHQWDWLRQ EUXOHUHSUHVHQWDWLRQ FUXOHUHSUHVHQWDWLRQ Fg dfferent rule representatons The performance for the tranng data s used as the ftness of a rule. The smplcty measure s added on the ftness functon to get comprehensble-szed classfcaton rules as follows. It s generally known that a smpler classfer s more general than complcated one wth the same accuracy for the tranng data. ftness of where 1 = = number of correct samples number of total tran number of nodes smplcty =, number of maxmum nodes w ndvdual weght for tranng rate,and w 2 = w1 + smplcty w data weght for smplcty 2

7 84 J.-H. Hong and S.-B. Cho Table 4. Expermental envronments Parameter Value (fnal) Parameter Value (fnal) Populaton sze 100 Mutaton rate 0.1~0.3 (0.2) Maxmum generaton 50,000 Permutaton rate 0.1 Selecton rate 0.6~0.8 (0.8) Maxmum depth of a tree 3 Crossover rate 0.6~0.8 (0.8) Eltsm yes 4 Experments 4.1 Expermental Envronment The proposed method s verfed wth Lymphoma cancer dataset, whch s well known mcroarray dataset [18]. Ths dataset ( s one of popular DNA mcroarray datasets used n bonformatcs for the benchmark. It conssts of 47 samples: 24 samples of GC B-lke and 23 samples of actvated B-lke. Each sample has 4,026 gene expresson levels. All features are normalzed from 0 to 1. Snce the gene expresson data consst of few samples wth many features, the proposed method s evaluated by leave-one-out cross-valdaton. Total 47 experments are conducted, where each sample s set as the test data and the others are set as the tran data. All experments are repeated 10 tmes and the average of them s used as the fnal result. The parameters for genetc programmng are set as shown n Table 4. We use roulette wheel selecton wth elte preservng strategy, and set the weghts w 1 and w 2 of the ftness evaluaton functon as 0.9 and 0.1, respectvely. 4.2 Results Analyss Fg. 5 shows the accuracy for the test data n terms of the rule representatons. We can get 96.6% test accuracy n average wth the thrd rule representaton although ths s the smplest among the three rule representatons. Fg. 6 shows the classfcaton rules whch are the most frequently occurred n the experments, whle they classfy all the samples correctly wth a few genes. The detaled descrptons of the genes are shown n Table 5~7. The functons of some genes are not known yet, and ths gves nterest to medcal experts to study the functons of those genes. Although the rules are obtaned by the cross-valdaton, we focus on the easy nterpretablty and the nformaton ncluded n the rules. The rule shown n Fg. 6(a) s analyzed based on the meanng of the arthmetc operatons as descrbed n Table 1. F4 affects a sample to be ncluded nto class 2 whle negatvely nto the class 1. F20 and F25 are combned by a multplcatve correlaton, so as to push samples to be classfed nto class1. We can nterpret t as follows so to obtan some nformaton from the rule:

8 Lymphoma Cancer Classfcaton Usng Genetc Programmng wth SNR Features 85 Fg. 5. The accuracy for test data È ) ) ) È) È) ) ) ) È) È) (a) 1 st rule (b) 2 nd rule (c) 3 rd rule Fg. 6. The rules for perfect classfcaton wth each rule representaton ) F4, F20, and F25 are related wth lymphoma cancer The value of F4 s negatvely related wth the cancer F20 and F25 are postvely related wth the cancer We have conducted an addtonal experment to compare the proposed method wth a neural network, one of promsng machne learnng technques. 3-layered multlayer perceptron s used wth 2~10 hdden nodes, 2 output nodes, learnng rate of 0.01~0.1 and momentum of 0.7~0.9. The maxmum teraton for learnng s fxed to Three features are used as the nput of the neural network. The tranng accuracy s 98%, whle the test accuracy s 97.8%. Even wth ntensve efforts, we could not get 100% accuracy wth the neural network. The neural network has been also learned wth 30 features, but the result s worse than the frst case. It just obtaned 95.7% tranng accuracy and 95.7% test accuracy. Ths proves that genetc programmng also selected useful features among the 30 features. The addtonal experment shows the compettve performance of the proposed method n the classfcaton of the dataset. Fg. 6(b) and Fg. 6(c) are rules for the 2 nd and the 3 rd rule representatons. Based on the analyss method, each classfcaton rule ncludes the followng nformaton.

9 86 J.-H. Hong and S.-B. Cho Table 5. The detaled descrpton of genes used n the rule shown n Fg. 6(a) Feature # Gene # Descrpton F20 75 F Unknown UG Hs ets varant gene 6 (TEL oncogene); Clone= , *core bndng factor alpha1b subunt=cbf alpha1=pebp2aa1 transcrpton factor =AML1 Proto-oncogene=translocated n acute myelod leukema; Clone=263251, F Unknown UG Hs ESTs; Clone=746300, Table 6. The detaled descrpton of the genes used n the rule shown n Fg. 6(b) Feature # Gene # F Descrpton CXCR5=BLR1=B-cell homng chemokne receptor=l1; Clone=31, 4297 F *FAK=focal adheson knase; Clone=795352, F29 86 *BCL-2; Clone=342181, F *CD10=CALLA=Neprlysn=enkepalnase; Clone=200814, Table 7. The detaled descrpton of genes used n the rule shown n Fg. 6(c) Feature # Gene # Descrpton F Unknown; Clone= , F *Unknown; Clone=825199, F Lymphotoxn-Beta=Tumor necross factor C; Clone= , F *Unknown; Clone= , The classfcaton rule n Fg. 6(b) can be nterpreted as follows: F18, F11, F29, and F1 are related wth the lymphoma cancer F18 and F29 affect postvely on the GC B-lke lymphoma cancer F11 and F1 are negatvely related wth the GC B-lke lymphoma cancer Each weght sgnfes the mportance on the cancer classfcaton The classfcaton rule n Fg. 6(c) can be nterpreted as follows: F24, F4, F14, and F17 are related wth the lymphoma cancer F24 and F17 affect postvely on the GC B-lke lymphoma cancer F4 and F14 are negatvely related wth the GC B-lke lymphoma cancer

10 Lymphoma Cancer Classfcaton Usng Genetc Programmng wth SNR Features 87 F29 used n the 2 nd rule s the *BCL-2 gene, whch turned out that t s related wth the lymphoma cancer [19]. F14 descrbed n Table 6 s known that t relates wth the lymphoma cancer. These mply that the rules dscovered by the proposed method are understandable, and there s a possblty that the other features are related wth the lymphoma cancer. These rules also need a demonstraton by medcal experts, but there s a good chance of dscoverng useful nformaton from them. 5 Concludng Remarks In ths paper, we have proposed an effectve rule generaton method, whch uses genetc programmng wth SNR features. Snce gene expresson data have huge-scale feature data wth a few samples, t s dffcult to generate valuable classfcaton rules from the data drectly. The SNR feature selecton method used n ths paper remarkably reduces the number of features, whle genetc programmng generates useful rules wth those features selected. Moreover we have proposed the analyss method for the arthmetc rule representaton. It s very smple but helpful for the nterpretaton of the rules extracted. The expermental results show that the performance of the proposed method s effectve to extract classfcaton rules wth 96.6% test accuracy, and also good classfcaton rules have been easly nterpreted and provded useful nformaton for the classfcaton. As the future work, we wll verfy the obtaned results wth medcal experts and try to combne logcal and arthmetc structures n genetc programmng for better classfcaton. Each structure has ts advantage, and the combnaton mght help to mprove the performance and nterpretablty. Acknowledgements. Ths work was supported by Bometrcs Engneerng Research Center, and a grant of Korea Health 21 R&D project, Mnstry of Health & Welfare, Republc of Korea. References 1. A. Ben-Dor, et al., "Tssue classfcaton wth gene expresson profles," J. of Computatonal Bology, vol. 7, pp , A. Brazma and J. Vlo, "Gene expresson data analyss," Federaton of European Bochemcal Socetes Letters, vol. 480, pp , C. Park and S.-B. Cho, "Genetc search for optmal ensemble of feature-classfer pars n DNA gene expresson profles," Int. Jont Conf. on Neural Networks, pp , K. Tan, et al., "Evolutonary computng for knowledge dscovery n medcal dagnoss," Artfcal Intellgence n Medcne, vol. 27, no. 2, pp , J. Qunlan, C4.5: Programs for Machne Learnng, Morgan Kaufmann, D. Goldberg, Genetc Algorthms n Search, Optmazaton, and Machne Learnng, Addson-Wesley, K. DeJong, et al., Usng genetc algorthms for concept learnng, Machne Learnng, vol. 13, pp , A. Fretas, "A survey of evolutonary algorthms for data mnng and knowledge dscovery," Advances n Evolutonary Computaton, pp , 2002.

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