TY - JOUR T1 - Development of a New Supervised Principal Component Analysis Based on Artificial Neural Networks in Gene Expression Data TT - Gen Ekspresyon Verilerinde Yapay Sinir Ağlarına Dayalı Yeni Bir Denetimli Temel Bileşenler Analizi’nin Geliştirilmesi AU - Türe, Mevlüt AU - Kurt Ömürlü, İmran PY - 2018 DA - January DO - 10.20515/otd.371882 JF - Osmangazi Tıp Dergisi PB - Eskişehir Osmangazi Üniversitesi WT - DergiPark SN - 1305-4953 SP - 20 EP - 27 VL - 40 IS - 1 LA - en AB - Theaim of this study is dimension reduction of multidimensional gene expressiondata using supervised principal component analysis (S-PCA) and –proposed as anew approach- supervised principal component analysis with artificial neuralnetworks (S-ANN-PCA) and to compare performances of these two methods by usingrandom survival forests (RSF). In simulation application 5000 genes weregenerated according to multivariate normal distribution and then survival timethat is correlated to these gene data were generated for 100 units. Simulationstep was carried out with 1000 repetitions. Inaddition, gene expression data for 240 individuals with extensive B-celllymphoma (DLBCL) were used. Dimension reduction was done using Wald statisticin selection of important genes. The new data sets obtained from the methodswere analyzed using RSF analysis.In the simulation application, it was obtainedthat the explanatoriness of S-PCA was significantly different from S-ANN-PCA(p<0.001). In the DLBCL data application, it was found that the error ratefor the S-PCA was 36.78% and 43% for the S-ANN-PCA as a result of RSF. Theimportance value of S-PCA method was found to be higher and its error rate wasfound to be lower than the other method.S-PCA performed better than S-ANN-PCAin analyzing gene expression data experiencing a multidimensional problem. KW - Dimension reduction; Neural networks; Supervised principal component analysis; Random survival forests; Gene expression N2 - Bu çalışmada,denetimli temel bileşenler analizi (D-TBA) ile yeni bir yaklaşım olarakönerilen yapay sinir ağlarıyla denetimli temel bileşenler analizi (D-YSA-TBA)kullanılarak çok boyutlu gen ekspresyon verilerinin boyutunun indirgenmesi verandom survival forests (RSF) analizi kullanılarak performanslarınkarşılaştırılması amaçlandı. Simülasyon uygulamasında çok değişkenli normaldağılımdan 100 birim için 5000 gen ve bu gen verisi ile ilişkili yaşam süresiverisi türetildi. Simülasyon aşaması 1000 tekrarlı olarak gerçekleştirildi.Ayrıca yaygın B-hücreli lenfoma (DLBCL) hastası 240 bireye ilişkin genekspresyon verileri kullanıldı. Önemli genlerin seçiminde Wald istatistiğikullanılarak boyut indirgemesi yapıldı. Yöntemlerden elde edilen yeni verisetleri RSF analizi kullanılarak analiz edildi. Simülasyon uygulamasında D-TBAve D-YSA-TBAyöntemlerinin açıklayıcılıkları arasında anlamlı bir fark olduğugörülmüştür (p<0.001). DLBCL verisi ile yapılan uygulamada D-TBA yöntemininhatasının %36.78, D-YSA-TBA yönteminin ise RSF sonucu- %43 olduğu bulunmuştur. D-TBA yönteminin önemdeğeri diğer yöntemden daha büyük, hatası ise daha düşük çıkmıştır. Çokboyutluluk problemi yaşanan gen ekspresyon verilerinin analizinde D-TBA, D-YSA-TBA’yagöre daha iyi performans göstermiştir. CR - 1. Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. New England Journal of Medicine. 2002;346(25):1937-47. CR - 2. Bair E, Tibshirani R. Semi-supervised methods to predict patient survival from gene expression data. 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UR - https://doi.org/10.20515/otd.371882 L1 - https://dergipark.org.tr/tr/download/article-file/429306 ER -