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veri madenciliği yöntemleriyle mikrodizilim gen ifade analizi ...

veri madenciliği yöntemleriyle mikrodizilim gen ifade analizi ...

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Veri madencili¤i yöntemleriyle <strong>mikrodizilim</strong> <strong>gen</strong> <strong>ifade</strong> <strong>analizi</strong>17. Ben-Dor A, Bruhn L, Friedman N, Nachman I, SchummerM, Yakhini N. Tissue classification with <strong>gen</strong>e expressionprofiles. Journal of Computational Biology 2000; 7:559-83.18. Jagota A. Microarray Data Analysis and Visualization, Bioinformaticsby the Bay Press, Santa Cruz, 2001.19. DNA Mikroarray/DNA Mikrodizilimi: Hematolojide KullanımAlanlar, Tayfun ÖZÇELIK, XXX. Ulusal HematolojiKongresi, Mezuniyet Sonrası Eğitim Kursu Dokümanı. Erişimadresi: http://www.thd.org.tr/doc/kurs_pdf/dna.pdf,Erişim tarihi: 16.11.2011.20. Doç. Dr. Hatice Mer<strong>gen</strong> kişisel internet sayfası. Erişim tarihi:10 Eylül 2011. Erişim adresi: http://yunus. hacettepe.edu.tr/~mer<strong>gen</strong>/derleme/d_microarray.pdf21. Frank E, Hall MA, Holmes G, Kirkby R, Pfahringer B. Witten,TriggL. Weka-a machine learning workbench for datamining. In: Maimon O, Rokach L (eds). The Data Miningand Knowledge Discovery Handbook, Springer 2005: 1305-14.22. Demsar J, Leban G, Zupan B, FreeViz. An Intelli<strong>gen</strong>t VisualizationApproach for Class-Labeled Multidimensional DataSets, Intelli<strong>gen</strong>t Data Analysis in Medicine and PharmacologyWorkshop-2005-Scotland, UK.23. Hyvärinen A, Oja E. Independent component analysis: algorithmsand application. Neural Networks 2000; 13:411-30.24. International Journal of Innovative Computing, Informationand Control ICIC International, Independent ComponentAnalysis for Classification of Remotely Sensed Images,2006; 2:31349-4198.25. Ulisses M. Braga-Neto1,3 and Edward R. Dougherty, Iscross-validation valid for small-sample microarray classification?Bioinformatics 2004; 20:374-80. doi: 10.1093/ bioinformatics/btg41926. Boulesteix AL, Strimmer K. Predicting transcription factoractivities from combined analysis of microarray and ChIPdata: a partial least squares approach, heoretical. Biologyand Medical Modelling 2005; 2:23.27. Jin X, Bie R. Random Forest and PCA for Self-OrganizingMaps Based Automatic Music Genre Discrimination, Conferenceon Data Mining, 2006: 414-7.28. Leo B. Random forests. Machine Learning 2001; 45:5-32.29. Shi T, Horvath S. Unsupervised learning with random forestpredictors. Journal of Computational and GraphicalStatistics 2006; 15:118-38.30. Amaratunga D, Cabrera J, Lee YS. Enriched random forests.Bioinformatic 2008; 24:2010-4.31. Huerta M, Cedano J, Querol E. Analysis of nonlinear relationsbetween expression profiles by the principal curves oforiented-points approach. J Bioinform Comput Biol 2008;6:367-86.32. Brier GW. Verification of forecasts expressed in terms ofprobability. Monthly weather review 1950; 78:1-3.33. Airola A. A comparison of AUC estimators in small-samplestudies. Machine Learning in Systems Biology 2010; 8:3-13.34. Vapnik V. Estimation of Dependences Based on EmpiricalData [in Russian]. Nauka, Moscow, 1979. (English translation:Springer, New York, 1982).35. Karabulut E, Karaağaoglu E. Biyoinformatik ve biyoistatistik.Hacettepe Tıp Dergisi 2010; 41:162-70.36. Alpar CR. Uygulamalı Çok Değişkenli İstatistiksel YöntemlereGiriş, Nobel Yayın Evi, Ocak 2003; ISBN: 9755914315.37. Bação F, Lobo V, Painho M. Self-organizing maps as substitutesfor K-Means Clusteringö Lecture Notes in Computer Science,2005; 3516/2005, 9-28, DOI: 10.1007/11428862_65.38. Wehrens R, Buydens LMC. Self and super-organizing mapsin R: the kohonen package. J Stat Soft 2007; 21:1-19.39. Bradley AP. The use of the area under the ROC curve in theevaluation of machine learning algorithms. Pattern Recognition1997; 30:1145:59.40. Vanderlooy S, Hullermeier E. A critical analysis of variantsof the AUC. Machine Learning 2008; 72:247:62.41. Waegeman W, De Baets B, Boullart L. ROC analysis in ordinalregression learning. Pattern Recognition Letters 2008;29:1:9.42. Baker S, Kramer B. Identifying <strong>gen</strong>es that contribute mostto good classification in microarrays. BMC Bioinformatics2006; 7:407.43. Gevaert O, Smet FD, Timmerman D, Moreau Y, Moor BD.Predicting the prognosis of breast cancer by integrating clinicaland microarray data with bayesian networks. Bioinformatics2006; 22:184-90.44. Cosgun E, Aksarı Y. GENE 3E: a new bioinformatics tool for<strong>gen</strong>etic data mining, Society for Design and Process Conference,June 12-16 2011, Jeju, South Korea.Cilt 42 • Say› 4 • 2011189

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