Methods for extracting marker genes that trigger the growth

of cancerous cells from a high level of complexity microarrays are of much interest from the computing community. Through the identified genes, the pathology of cancerous cells can be revealed and early precaution

can be taken to prevent further proliferation of cancerous cells. In this paper, we propose an innovative hybridised gene identification framework based on genetic algorithms and neural networks to identify marker genes for leukaemia disease. Our approach confirms that high classification

accuracy does not ensure the optimal set of genes have been identified and our model delivers a more promising set of genes even with a lower classification accurac

Mintram, Robert

Phalp, Keith T.

Schierz, Amanda C.

Tong, Dong L.

English

Bournemouth University Research Online

Innovative hybridisation of genetic algorithmsand neural networks in detecting marker genesfor leukaemia cancerDong Ling Tong1, Keith Phalp1, Amanda Schierz1 and Robert Mintram21 Bournemouth University, School of Design, Engineering and Computing, PooleHouse, Talbot Campus, Fern Barrow, Poole, Dorset, BH12 5BB, UK2 Personal contactAbstract. The hybridisation of genetic algorithm (GAs) and artificialneural network (ANNs) are not new for microarray studies. However,these hybrid systems require data preprocessing and focus on classifi-cation accuracy. In this paper, a feature selection method based on thehybridisation of GAs and ANNs for oligonucleotide microarray data isproposed. The fitness values of the GA chromosomes are defined as thenumber of correctly labelled samples returned by the ANN. For gene val-idation, three supervised classifiers have been employed to evaluate thesignificance of selected genes based on a separate set of unknown sam-ple data. Experimental results show that our method is able to extractinformative genes without data preprocessing and this has reduced thegene variability errors in the selection process.1 IntroductionThe main challenge when working with microarray data is to identify compu-tationally effective and biologically meaningful analysis models that extract themost informative and unbiased marker genes from a large pool of genes thatare not involved in the array experiments. Numerous genetic algorithm (GA)and neural network (ANN) hybrid systems have been developed to emphasizeeffective classification [1–3, 8, 11]. Beiko and Charlebois [1] utilised the evolu-tionary ability of GAs to identify the best combinations of sequence indices andANN architecture for DNA sequence classification. Meanwhile, Karzynski et al.[8] used GAs to optimise both the architecture and weight assignment of ANNsfor multiclass recognition. In addition to optimising ANNs using GAs, recentstudies tend to utilise the universal computational power of ANNs to computeGA fitness function for cancer classification. For instance, Bevilacqua et al. [2]and Lin et al. [11] applied the error rates returned by ANN to determine thefitness of GA chromosome in the classification of breast cancer metastasis recur-rence and multiclass microarray datasets, respectively. Cho et al. [3] defined GAfitness based on ANN classification results on SRBCTs tumour data.Instead of improving classification performance, our work is focused on fea-ture selection for oligonucleotide microarray data using GAs and ANNs. For geneselection, the fitness values of GA chromosomes are defined as the number ofcorrectly labelled samples returned by a feedforward ANN. To show the selec-tion performance, the same experimental dataset as Golub et al. [7] is used. Forgene validation, some commonly applied classifiers, such as multilayer percep-tron (MLP), support vector machine (SVM) and nearest neighbour (KNN) havebeen employed using the WEKA suite of data mining software and the selectionresults are compared with previous experiments based on the same experimentaldataset.The rest of the paper is organised as follows. Section 2 describes proposed fea-ture selection model. The experiment results are presented in Section 3. Finallyin Section 4, the conclusion is drawn.2 Methods2.1 Feature Selection ModelThe proposed model has three components: population initialisation, fitness com-putation and pattern evaluation.Population initialisation A set of features from the experimental dataset israndomly chosen by the GA. These features form a finite feature space and areused as a basis for the fitness computation of each member of the population.To find the optimal population size, preliminary experiments were conducted on4 variants of population sizes: 50, 100, 200 and 300. The population size of 300was found to be the most optimal. Each chromosome is represented by 10 genesexpressions which is encoded with a real number representation.Fitness computation The fitness function is the number of correctly labelledsamples returned by the ANN as shown in equation Eq. 1. For proposed method,chromosomes with higher fitness values are more likely to survive than those withleast values.fitness =∑i=1n∑k=1csik, (1)sik = tik −√(aik − cik)2{1, oik = tik0, oik 6= tik , (2)Ck =1sk∑s²kask, (3)where sik, tik, aik, oik and ck represent sample data, target value of thesample, network activation output, actual output value and class centroid value,respectively. A feedforward ANN with the structure of 10-5-2 and network sizeof 67 including 5 and 2 bias nodes in the hidden and output layers, respectively,is constructed. The tanh activation function is employed as it is one of thecommonly used nonlinear functions in the ANN.Pattern evaluation Two sets of evaluation were performed using the GA:feature evaluation and network evaluation. For feature evaluation, two parentchromosomes are crossovered to produce a new offspring which is then mutatedto create diversity from its parent. For network evaluation, two parent networksare crossovered to form a new set of network weights which then will be usedto compute the fitness value of feature offspring. To retain the best chromo-some set in each generation, an elitism scheme was applied in which only 1chromosome will be replaced in each generation. For genetic operations, binarytournament selection, single-point crossover and simple mutation are used. Therates of crossover and mutation are 0.5 and 0.1, respectively.Termination criteria Two termination criteria are defined: criteria (A) is usedto stop the entire selection process and criteria (B) is used to stop the fitnessevaluation. Both criteria (A) and (B) were set to 5000 and 20000 repetitions,respectively.2.2 Data AcquisitionFor performance evaluation, the acute leukaemia dataset [7] which contains acutelymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML) tumourdata are considered. There are 72 samples and 7129 gene expression levels inthe dataset. Among 72 sample data, 38 samples (27 ALL, 11 AML) are usedfor gene selection and the remaining 34 samples (20 ALL, 14 AML) are used forvalidating the significance of the selected gene subsets. To assess the efficacy ofthe proposed method, the data normalisation process is ignored.2.3 ClassificationIn order to validate the significance of the selected genes, 3 supervised clas-sifiers: MLP, SMO and IBk; are employed using WEKA suite of data mining(http://www.cs.waikato.ac.nz/ml/weka/). The MLP is a 3-layered backpropa-gation perceptron-based model that employs a sigmoid activation function; theSMO is an implementation of support vector machines (SVMs) that computethe upper bound of support vector weights using a sequential minimal optimi-sation algorithm; the IBk is an implementation of a k-nearest neighbour (KNN)classifier that employs the similarity distance metric in forming neighbours. Allclassifiers are implemented with the default settings except for the IBk algorithmwhere k is set to 3.3 Results and Discussions3.1 Gene Selection ResultsBy repeating the selection process 5000 times using the training set, the top 48genes based on the selection frequency of at least 50 selections and above areranked. Table 1 shows the comparison of the selected genes from our method andprevious works on the experiment dataset. Out of the 48 selected genes, 28 areconsistent with Golub et al. [7]. The significant genes such as CST3 (M27891),zyxin (X95735), c-myb (U22376), adipsin (M84526), CCND3 (M92287), mac-marcks (HG1612-HT1612), proteasome IOTA chain (X59417), IL 8 (M28130),azurocidin (M96326) and IL 8 precursor (Y00787) are highly ranked by ourmethod. Amongst these genes, CST3 and zyxin genes have been reported as thestrongest predictors in related literature [4, 5, 7, 10, 12].3.2 Classification ResultsAs previously described, 34 out of 72 samples are used to assess the significanceof the selected genes based on the 3 supervised classifiers. Table 2 shows the clas-sification results based on varying number of selected genes. 33/34 test sampleshave been correctly classified with an accuracy of 97.06% when there are at least8 selected genes. The classification performance is reduced when there are lessthan 8 genes used for class discrimination. Although genes M27891 and X95735have been identified as the 2 strongest predictors in literature, however, the clas-sification performance based on these combined genes are 91.18%, 85.29% and91.18% for MLP, SMO and IBk, respectively. By increasing the number of se-lected genes for classification, the performance has been improved. This confirmsthat there are many strong predictors that can be used for classification and thissupports the similar observations on works [7, 10].3.3 Related WorksTable 3 shows some related research on the microarray experiment dataset. Sev-eral selection techniques have been applied to find the optimal set of genes,including signal-to noise (S2N), Pearson correlations, correspondence analysis(COA), principal component analysis (PCA), between-group/within-group ra-tio (BSS/WSS) and recursive feature elimination (RFE). Some produce smallamount of genes in the selection process and some require more genes for bettersearch performance. However, all existing selection methods based on oligonu-cleotide microarray data require data preprocessing for optimal search. Depend-ing on the selection approach and classification method, varying data preprocess-ing techniques are implemented. This has contributes to the genes variability onselection results. Our method, on the other hand, has the ability to extract in-formative genes without data preprocessing which reduces variation errors onthe selected genes.4 ConclusionsIn this paper, a feature selection method based on genetic algorithms and neuralnetworks for oligonucleotide microarray data was developed. For the selectionprocess, the fitness value of the selected gene subset is based on the correctlyTable 1. Comparison of top-48 selected genes by proposed method and previous works.The gene selection is based on the selection frequency of 50 times or more. Genes inItalic had been reported by Golub et al. [7].Rank Index Acc No Description MarkerGenes1 1882 M27891 CST3 Cystatin C abcd2 4847 X95735 Zyxin acd3 5772 U22376 C-myb gene abc4 2288 M84526 Adipsin ad5 2354 M92287 CCND3 Cyclin D3 ab6 804 HG1612-HT1612Macmarcks7 4328 X59417 Proteasome IOTA chain ab8 6200 M28130 Interleukin 8 (IL8) gene abd9 2402 M96326 Azurocidin gene abd10 6201 Y00787 Interleukin 8 precursor abd11 2121 M63138 CTSD Cathepsin D a12 1120 J04615 SNRPN Small nuclear ribonucleoprotein polypeptide N13 5552 L06797 Probable G protein-coupled receptor LCR1 homolog b14 5501 Z15115 TOP2B Topoisomerase (DNA) II beta b15 1704 M13792 ADA Adenosine deaminase b16 6041 L09209 APLP2 Amyloid beta (A4) precursor-like protein 217 4211 X51521 VIL2 Villin 218 1928 M31303 Oncoprotein 18 (Op18) gene a19 4373 X62320 GRN Granulin20 1745 M16038 LYN V-yes-1 Yamaguchi sarcoma viral related oncogenehomologac21 2642 U05259 MB-1 gene bd22 6218 M27783 ELA2 Elastatse 2, neutrophil cd23 760 D88422 Cystatin A cd24 4377 X62654 ME491 gene for Me491/CD63 antigen25 6539 X85116 Epb72 gene exon 1 a26 3320 U50136 Leukotriene C4 synthase (LTC4S) gene ac27 1685 M11722 Terminal transferase mRNA bd28 4535 X74262 Retinoblastoma binding protein P4829 5039 Y12670 LEPR Leptin receptor ac30 5191 Z69881 Adenosine triphosphatase, calcium31 1779 M19507 MPO Myeloperoxidase bd32 4052 X04085 Catalase (EC 1.11.1.6) 5’flank and exon 1 mapping tochromosome 11, band p13 (and joined CDS)33 1829 M22960 PPGB Protective protein for beta-galactosidase (galac-tosialidosis)34 6378 M83667 NF-IL6-beta protein mRNA d35 3258 U46751 Phosphotyrosine independent ligand p62 for the Lck SH2domain mRNAa36 1133 J04990 Cathepsin G precursor b37 2020 M55150 FAH Fumarylacetoacetate ac38 6376 M83652 PFC Properdin P factor, complement d39 5954 Y00339 CA2 Carbonic anhydrase II40 6277 M30703 Amphiregulin (AR) gene41 6308 M57731 GRO2 oncogene d42 312 D26308 NADPH-flavin reductase43 1249 L08246 Induced myeloid leukemia cell differentiation proteinMCL1a44 6855 M31523 TCF3 Transcription factor 345 3056 U32944 Cytoplasmic dynein light chain 1 (hdlc1) mRNA46 878 HG2855-HT2995Heat Shock Protein, 70 Kda (Gb:Y00371)47 1630 L47738 Inducible protein mRNA48 1962 M33680 26-kDa cell surface protein TAPA-1 mRNA ba represents marker genes reported by Cho and Won [4]b represents marker genes reported by Culhane et al. [5]c represents marker genes reported by Li and Yang [10]d represents marker genes reported by Mao et al. [12]Table 2. Classification performance comparison based on the number of genes used inthe test set.Number of genes MLP SMO IBk (k=3)top 2 91.18 85.29 91.18top 4 94.12 97.06 94.12top 8 97.06 97.06 97.06top 16 97.06 97.06 97.06top 32 97.06 97.06 97.06top 48 97.06 97.06 97.06Table 3. Some relevant works on acute leukaemia.Authors SelectionmethodClassificationmethodData preprocessing step MarkergenesidentifiedProposed method GANN - - 48Golub et al. [7] S2N WV mean and deviation normal-isation50Cho and Won [4] Pearson ensemble MLPs max-min normalisation 50Culhane et al. [5] COA, PCA BGA for COA: negative valuestransformation; for PCA:mean and deviation normal-isation50Li and Yang [10] stepwiseselectionlogistic regression log transformation 12Dudoit et al. [6] BSS/WSS various discriminantmethodsthresholding, filtering, logtransformation, mean andvariance normalisation-Mao et al. [12] RFE, F-test SVMs preprocessing step used in[6]20Lee and Lee [9] BSS/WSS SVMs preprocessing step used in[6]20labelled sample data computed by neural networks. For performance evaluation,the selected genes have been evaluated by implementing commonly used classi-fiers based on a separate set of unknown sample data. The experimental resultsshow that our method is able to identify a set of informative genes without datapreprocessing this has reduced the potential of gene variability problems.References1. Beiko, R.G., Charlebois, R.L.: GANN: Genetic algorithm neural networks for thedetection of conserved combinations of features in DNA. BMC Bioinformatics 6,(2005)2. Bevilacqua, V., Mastronardi, G., Menolascina, F.: Genetic Algorithm and NeuralNetwork Based Classification in Microarrat Data Analysis with Biological ValidityAssessment. ICIC 3, 475–484 (2006)3. Cho, H.S., Kim, T.S., Wee, J.W., Jeon, S.M., Lee, C.H.: cDNA microarray databased classification of cancers using neural networks and genetic algorithms. Nan-otech’03: Nanotechnology Conference and Trade Show, proceedings, (2003)4. Cho, S.B., Won, H-H.: Cancer classification using ensemble of neural networks withmultiple significant gene subsets. Applied Intelligence 26, 243–250 (2007)5. Culhane, A.C., Perrie`re, G., Considine, E.C., Cotter, T.G., Higgins, D.G.: Between-group analysis for microarray data. Bioinformatics 18, 1600–1608 (2002)6. Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of Discrimination Methods forthe Classification of Tumors Using Gene Expression Data. Journal of the AmericanStatistical Association 97, 77–87 (2002)7. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P.,Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander,E.S.: Molecular Classification of Cancer: Class Discovery and Class Prediction byGene Expression Monitoring. Science 286, 531–536 (1999)8. Karzynski, M., Mateos, A´., Herrero J., Dopazo, J.: Using a Genetic Algorithm and aPerceptron forFeature Selection and Supervised Class Learning in DNA MicroarrayData. Artif. Intell. Rev. 20, 39–51 (2003)9. Lee, Y., Lee, C-K.: Classification of multiple cancer types by multicategory supportvector machines using gene expression data. Bioinformatics 19, 1132–1139 (2003)10. Li, W, Yang, Y.: How Many Genes Are Needed for a Discriminant MicroarrayData Analysis? CAMDA’00: Critical Assessment of Techniques for Microarray DataAnalysis, proceedings, 137–150 (2002)11. Lin, T-C., Liu, R-S., Chao Y-T., Chen, S-Y.: Multiclass Microarray Data Classi-fication Using GA/ANN Method. PRICAI’06: Trends in Artificial Intelligence, 9thPacific Rim International Conference on Artificial Intelligence, proceedings 4099,1037–1041 (2006)12. Mao, Y., Zhou, X., Pi, D., Sun, Y., Wong, S.T.C.: Multiclass Cancer Classificationby Using Fuzzy Support Vector Machine and Binary Decision Tree With GeneSelection. Journal of Biomedicine and Biotechnology 2005, 160–171 (2005)

C-K.: Classi¯cation of multiple cancer types by multicategory support vector machines using gene expression data.

C.H.: cDNA microarray data based classi¯cation of cancers using neural networks and genetic algorithms.

D.G.: Betweengroup analysis for microarray data.

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA.

Genetic Algorithm and Neural Network Based Classi¯cation in Microarrat Data Analysis with Biological Validity Assessment.

H-H.: Cancer classi¯cation using ensemble of neural networks with multiple signi¯cant gene subsets.

How Many Genes Are Needed for a Discriminant Microarray Data Analysis? CAMDA'00: Critical Assessment of Techniques for Microarray Data Analysis, proceedings,

Molecular Classi¯cation of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring.

S-Y.: Multiclass Microarray Data Classi¯cation Using

S.T.C.: Multiclass Cancer Classi¯cation by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection.

T.P.: Comparison of Discrimination Methods for the Classi¯cation of Tumors Using Gene Expression Data.

Using a Genetic Algorithm and a Perceptron forFeature Selection and Supervised Class Learning

Using a Genetic Algorithm and a Perceptron forFeature Selection and Supervised Class Learning in

Innovative Hybridisation of Genetic Algorithms and Neural Networks in Detecting Marker Genes for Leukaemia Cancer

Innovative Hybridisation of Genetic Algorithms and Neural Networks in Detecting Marker Genes for Leukaemia Cancer

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