In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli
Yıl: 2021 Cilt: 25 Sayı: 2 Sayfa Aralığı: 365 - 370 Metin Dili: Türkçe DOI: 10.19113/sdufenbed.867067 İndeks Tarihi: 29-07-2022
In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli
Öz: Toksisite testleri arasında, bir etken nedeniyle ortaya çıkabilecek genetikdeğişim (mutasyon) olarak tanımlanabilen mutajenisite önemli yer tutmaktadır. Buçalışmada genel olarak mutajenisite belirleme sürecini iyileştirebilmek adına insilico yaklaşım kapsamında istatistiksel öğrenme algoritmaları kullanılmıştır. Sözkonusu yaklaşım deneyler ile elde edilen mutajenisite bilgisi içeren molekül setineuygulanmış ve dikkate değer sınıflama başarıları elde edilmiştir. Çalışmadakullanılmak üzere literatürde bulunan, moleküllerden oluşan Bursi ile Benchmarkveri setleri birleştirilmiş ve Molecular Operating Environment (MOE) programıaracılığı ile moleküllerin özellikleri hesaplanmıştır. Hesaplama sonucunda 10835gözleme ve 193 değişkene sahip veri seti üzerinde karar ağaçları algoritmalarıuygulanarak grid arama yaklaşımı ile parametre seçimi gerçekleştirilmiştir. Eldeedilen en iyi parametreler ile kurulan modeller sonucunda değişkenlerin seçimimutajenisiteyi tahmin etmedeki önem düzeylerine göre yapılmış ve verininboyutu en etkili 72 değişkene indirgenmiştir. Seçilen değişkenlerden oluşan yeniveriye farklı istatistiksel öğrenme algoritmaları uygulanmış ve içlerinden en iyisonuç veren beş sınıflama algoritmasına karar verilmiştir. Parametre eniyilemesi ile model başarımları arttırılan bu algoritmalar kullanılarak yaklaşık %90mutajenisiteyi doğru sınıflama oranları elde edilmiştir.
Anahtar Kelime: Statistical Learning Model for In-Silico Mutagenicity Prediction
Öz: Among the toxicity tests, mutagenicity defined as a genetic change that can occur due to an agent, has an important place. In this study, statistical learning algorithms were used within the scope of in-silico approach in order to improve the mutagenicity determination process in general. This approach has been applied to the set of molecules containing mutagenicity information obtained by experiments and remarkable classification success were achieved. In order to use in this study, Bursi and Benchmark data sets consisting of molecules found in the literature were combined and the properties of molecules were calculated by means of the Molecular Operating Environment (MOE). As a result of the calculation, decision trees algorithms were applied on the data set with 10835 molecules and 193 variables and parameter selection was performed with grid search approach. The selection of variables was made according to their level of importance in predicting mutagenicity as a result of models established with the best parameters obtained, and the number of descriptors variables was reduced to the 72 most effective descriptor variables. Various statistical learning algorithms were applied to the reduced data set consisting of the selected variables, and five classification algorithms with the best results were decided. By the algorithms whose model performances were increased by means of parameter optimization, accurate prediction rates were obtained approximately 90% for mutagenicity classification.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | Gumustas E, Çakmak Pehlivanlı A (2021). In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli. , 365 - 370. 10.19113/sdufenbed.867067 |
| Chicago | Gumustas Enis,Çakmak Pehlivanlı Ayça In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli. (2021): 365 - 370. 10.19113/sdufenbed.867067 |
| MLA | Gumustas Enis,Çakmak Pehlivanlı Ayça In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli. , 2021, ss.365 - 370. 10.19113/sdufenbed.867067 |
| AMA | Gumustas E,Çakmak Pehlivanlı A In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli. . 2021; 365 - 370. 10.19113/sdufenbed.867067 |
| Vancouver | Gumustas E,Çakmak Pehlivanlı A In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli. . 2021; 365 - 370. 10.19113/sdufenbed.867067 |
| IEEE | Gumustas E,Çakmak Pehlivanlı A "In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli." , ss.365 - 370, 2021. 10.19113/sdufenbed.867067 |
| ISNAD | Gumustas, Enis - Çakmak Pehlivanlı, Ayça. "In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli". (2021), 365-370. https://doi.org/10.19113/sdufenbed.867067 |
| APA | Gumustas E, Çakmak Pehlivanlı A (2021). In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(2), 365 - 370. 10.19113/sdufenbed.867067 |
| Chicago | Gumustas Enis,Çakmak Pehlivanlı Ayça In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 25, no.2 (2021): 365 - 370. 10.19113/sdufenbed.867067 |
| MLA | Gumustas Enis,Çakmak Pehlivanlı Ayça In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol.25, no.2, 2021, ss.365 - 370. 10.19113/sdufenbed.867067 |
| AMA | Gumustas E,Çakmak Pehlivanlı A In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2021; 25(2): 365 - 370. 10.19113/sdufenbed.867067 |
| Vancouver | Gumustas E,Çakmak Pehlivanlı A In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2021; 25(2): 365 - 370. 10.19113/sdufenbed.867067 |
| IEEE | Gumustas E,Çakmak Pehlivanlı A "In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli." Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25, ss.365 - 370, 2021. 10.19113/sdufenbed.867067 |
| ISNAD | Gumustas, Enis - Çakmak Pehlivanlı, Ayça. "In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli". Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 25/2 (2021), 365-370. https://doi.org/10.19113/sdufenbed.867067 |
