In-Silico Mutajenisite Tahmininde İstatistiksel Öğrenme Modeli

Year 2021, Volume: 25 Issue: 2, 365 - 370, 20.08.2021

Enis Gümüştaş Ayça Çakmak Pehlivanlı

https://doi.org/10.19113/sdufenbed.867067

Cited By: 1

Abstract

Toksisite testleri arasında, bir etken nedeniyle ortaya çıkabilecek genetik değişim (mutasyon) olarak tanımlanabilen mutajenisite önemli yer tutmaktadır. Bu çalışmada genel olarak mutajenisite belirleme sürecini iyileştirebilmek adına in-silico yaklaşım kapsamında istatistiksel öğrenme algoritmaları kullanılmıştır. Söz konusu yaklaşım deneyler ile elde edilen mutajenisite bilgisi içeren molekül setine uygulanmış ve dikkate değer sınıflama başarıları elde edilmiştir. Çalışmada kullanılmak üzere literatürde bulunan, moleküllerden oluşan Bursi ile Benchmark veri setleri birleştirilmiş ve Molecular Operating Environment (MOE) programı aracılığı ile moleküllerin özellikleri hesaplanmıştır. Hesaplama sonucunda 10835 gözleme ve 193 değişkene sahip veri seti üzerinde karar ağaçları algoritmaları uygulanarak grid arama yaklaşımı ile parametre seçimi gerçekleştirilmiştir. Elde edilen en iyi parametreler ile kurulan modeller sonucunda değişkenlerin seçimi mutajenisiteyi tahmin etmedeki önem düzeylerine göre yapılmış ve verinin boyutu en etkili 72 değişkene indirgenmiştir. Seçilen değişkenlerden oluşan yeni veriye farklı istatistiksel öğrenme algoritmaları uygulanmış ve içlerinden en iyi sonuç veren beş sınıflama algoritmasına karar verilmiştir. Parametre en iyilemesi ile model başarımları arttırılan bu algoritmalar kullanılarak yaklaşık %90 mutajenisiteyi doğru sınıflama oranları elde edilmiştir.

Keywords

Sınıflama, Topluluk Öğrenmesi, XGBoost, LightGBM, Değişken Seçimi, Toksisite

Supporting Institution

Mimar Sinan Güzel Sanatlar Üniversitesi

Project Number

2018-30

Thanks

Bu çalışma, Mimar Sinan Güzel Sanatlar Üniversitesi Bilimsel Araştırma Projeleri Komisyonu tarafından desteklenmiştir. (Proje No: 2018-30).

Statistical Learning Model for In-Silico Mutagenicity Prediction

Abstract

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.

Keywords

Classification, Ensemble Learning, XGBoost, LightGBM, Feature Selection, Toxicity

Project Number

2018-30

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