Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data
Yıl: 2018 Cilt: 40 Sayı: 1 Sayfa Aralığı: 20 - 25 Metin Dili: İngilizce DOI: 10.20515/otd.371882 İndeks Tarihi: 28-12-2018
Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data
Öz: The aim of this study is dimension reduction of multidimensional gene expression data using supervisedprincipal component analysis (S-PCA) and –proposed as a new approach- supervised principal component analysiswith artificial neural networks (S-ANN-PCA) and to compare performances of these two methods by using randomsurvival forests (RSF). In simulation application 5000 genes were generated according to multivariate normaldistribution and then survival time that is correlated to these gene data were generated for 100 units. Simulation stepwas carried out with 1000 repetitions.In addition, gene expression data for 240 individuals with extensive B-cell lymphoma (DLBCL) were used.Dimension reduction was done using Wald statistic in selection of important genes. The new data sets obtained fromthe methods were analyzed using RSF analysis.In the simulation application, it was obtained that the explanatorinessof S-PCA was significantly different from S-ANN-PCA (p<0.001). In the DLBCL data application, it was found thatthe error rate for the S-PCA was 36.78% and 43% for the S-ANN-PCA as a result of RSF. The importance value ofS-PCA method was found to be higher and its error rate was found to be lower than the other method.S-PCAperformed better than S-ANN-PCA in analyzing gene expression data experiencing a multidimensional problem
Anahtar Kelime: Konular:
Gen Ekspresyon Verilerinde Yapay Sinir Ağlarına Dayalı Denetimli Temel Bileşenler Analizi Yaklaşımı
Öz: Bu çalışmada, denetimli temel bileşenler analizi (D-TBA) ile yeni bir yaklaşım olarak önerilen yapay sinir ağlarıyla denetimli temel bileşenler analizi (D-YSA-TBA) kullanılarak çok boyutlu gen ekspresyon verilerinin boyutunun indirgenmesi ve random survival forests (RSF) analizi kullanılarak performansların karşılaştırılması amaçlandı. Simülasyon uygulamasında çok değişkenli normal dağılımdan 100 birim için 5000 gen ve bu gen verisi ile ilişkili yaşam süresi verisi türetildi. Simülasyon aşaması 1000 tekrarlı olarak gerçekleştirildi. Ayrıca yaygın B-hücreli lenfoma (DLBCL) hastası 240 bireye ilişkin gen ekspresyon verileri kullanıldı. Önemli genlerin seçiminde Wald istatistiği kullanılarak boyut indirgemesi yapıldı. Yöntemlerden elde edilen yeni veri setleri RSF analizi kullanılarak analiz edildi. Simülasyon uygulamasında D-TBA ve D-YSA-TBAyöntemlerinin açıklayıcılıkları arasında anlamlı bir fark olduğu görülmüştür (p<0.001). DLBCL verisi ile yapılan uygulamada D-TBA yönteminin hatasının %36.78, DYSA- TBA yönteminin ise RSF sonucu- %43 olduğu bulunmuştur. D-TBA yönteminin önem değeri diğer yöntemden daha büyük, hatası ise daha düşük çıkmıştır. Çok boyutluluk problemi yaşanan gen ekspresyon verilerinin analizinde D-TBA, D-YSA-TBA’ya göre daha iyi performans göstermiştir.
Anahtar Kelime: Konular:
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | TÜRE M, KURT ÖMÜRLÜ İ (2018). Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data. , 20 - 25. 10.20515/otd.371882 |
| Chicago | TÜRE Mevlüt,KURT ÖMÜRLÜ İMRAN Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data. (2018): 20 - 25. 10.20515/otd.371882 |
| MLA | TÜRE Mevlüt,KURT ÖMÜRLÜ İMRAN Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data. , 2018, ss.20 - 25. 10.20515/otd.371882 |
| AMA | TÜRE M,KURT ÖMÜRLÜ İ Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data. . 2018; 20 - 25. 10.20515/otd.371882 |
| Vancouver | TÜRE M,KURT ÖMÜRLÜ İ Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data. . 2018; 20 - 25. 10.20515/otd.371882 |
| IEEE | TÜRE M,KURT ÖMÜRLÜ İ "Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data." , ss.20 - 25, 2018. 10.20515/otd.371882 |
| ISNAD | TÜRE, Mevlüt - KURT ÖMÜRLÜ, İMRAN. "Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data". (2018), 20-25. https://doi.org/10.20515/otd.371882 |
| APA | TÜRE M, KURT ÖMÜRLÜ İ (2018). Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data. Osmangazi Tıp Dergisi, 40(1), 20 - 25. 10.20515/otd.371882 |
| Chicago | TÜRE Mevlüt,KURT ÖMÜRLÜ İMRAN Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data. Osmangazi Tıp Dergisi 40, no.1 (2018): 20 - 25. 10.20515/otd.371882 |
| MLA | TÜRE Mevlüt,KURT ÖMÜRLÜ İMRAN Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data. Osmangazi Tıp Dergisi, vol.40, no.1, 2018, ss.20 - 25. 10.20515/otd.371882 |
| AMA | TÜRE M,KURT ÖMÜRLÜ İ Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data. Osmangazi Tıp Dergisi. 2018; 40(1): 20 - 25. 10.20515/otd.371882 |
| Vancouver | TÜRE M,KURT ÖMÜRLÜ İ Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data. Osmangazi Tıp Dergisi. 2018; 40(1): 20 - 25. 10.20515/otd.371882 |
| IEEE | TÜRE M,KURT ÖMÜRLÜ İ "Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data." Osmangazi Tıp Dergisi, 40, ss.20 - 25, 2018. 10.20515/otd.371882 |
| ISNAD | TÜRE, Mevlüt - KURT ÖMÜRLÜ, İMRAN. "Supervised Principal Component Analysis Approach Based on Artificial Neural Networks in Gene Expression Data". Osmangazi Tıp Dergisi 40/1 (2018), 20-25. https://doi.org/10.20515/otd.371882 |
