Artificial Intelligence-based Colon Cancer Prediction by Identifying Genomic Biomarkers

Year 2022, Volume: 4 Issue: 2, 196 - 202, 01.05.2022

Nur Paksoy , Fatma Hilal Yağın

https://doi.org/10.37990/medr.1077024

Cited By: 12

Abstract

Aim: Colon cancer is the third most common type of cancer worldwide. Because of the poor prognosis and unclear preoperative staging, genetic biomarkers have become more important in the diagnosis and treatment of the disease. In this study, we aimed to determine the biomarker candidate genes for colon cancer and to develop a model that can predict colon cancer based on these genes.
Material and Methods: In the study, a dataset containing the expression levels of 2000 genes from 62 different samples (22 healthy and 40 tumor tissues) obtained by the Princeton University Gene Expression Project and shared in the figshare database was used. Data were summarized as mean ± standard deviation. Independent Samples T-Test was used for statistical analysis. The SMOTE method was applied before the feature selection to eliminate the class imbalance problem in the dataset. The 13 most important genes that may be associated with colon cancer were selected with the LASSO feature selection method. Random Forest (RF), Decision Tree (DT), and Gaussian Naive Bayes methods were used in the modeling phase.
Results: All 13 genes selected by LASSO had a statistically significant difference between normal and tumor samples. In the model created with RF, all the accuracy, specificity, f1-score, sensitivity, negative and positive predictive values were calculated as 1. The RF method offered the highest performance when compared to DT and Gaussian Naive Bayes.
Conclusion: In the study, we identified the genomic biomarkers of colon cancer and classified the disease with a high-performance model. According to our results, it can be recommended to use the LASSO+RF approach when modeling high-dimensional microarray data. 

Keywords

Colon cancer, microarray, genomics, LASSO, random forest, decision tree, gaussian naive bayes

Genomik Biyobelirteçleri Belirleyerek Yapay Zeka Tabanlı Kolon Kanseri Tahmini

Abstract

Amaç: Kolon kanseri dünya genelinde en sık görülen üçüncü kanser türüdür. Kötü prognoz ve net olmayan preoperatif evreleme nedeniyle, hastalığın tanı ve tedavisinde genetik biyobelirteçler daha önemli hale gelmiştir. Bu çalışmada kolon kanseri için biyobelirteç adayı genlerin belirlenmesi ve bu genlere dayalı olarak kolon kanserini başarılı bir şekilde tahmin eden bir modelin geliştirilmesi amaçlanmıştır.
Materyal ve Metot: Çalışmada, Princeton Üniversitesi Gen Ekspresyon Projesi ile elde edilen ve figshare veri tabanında paylaşılan 62 farklı örnekten (22 sağlıklı ve 40 tümör dokusu) 2000 genin ekspresyon düzeylerini içeren bir veri seti kullanıldı. Veriler ortalama ± standart sapma olarak özetlendi. İstatistiksel analizler için bağımsız örneklerde T-testi kullanıldı. Veri setindeki sınıf dengesizliği sorununu ortadan kaldırmak için öznitelik seçiminden önce SMOTE yöntemi uygulandı. Kolon kanseri ile ilişkili olabilecek en önemli 13 gen, LASSO öznitelik seçim yöntemi ile seçildi. Modelleme aşamasında Rastgele Orman (RF), Karar Ağacı (DT) ve Gauss naive Bayes yöntemleri kullanıldı.
Bulgular: LASSO tarafından seçilen 13 genin tümü, normal ve tümör numuneleri arasında istatistiksel olarak anlamlı bir farka sahipti. RF ile oluşturulan modelde doğruluk, seçicilik, f1-skor, duyarlılık, negatif ve pozitif prediktif değerlerinin tümü 1 olarak hesaplanmıştır. DT ve Gaussian Naive Bayes ile karşılaştırıldığında RF yöntemi en yüksek performansı vermiştir.
Sonuç: Çalışmada kolon kanserinin genomik biyobelirteçlerini belirledik ve hastalığı yüksek performanslı bir model ile sınıflandırdık. Elde ettiğimiz sonuçlara göre, yüksek boyutlu mikrodizi verilerinin modellenmesinde LASSO+RF yaklaşımının kullanılması önerilebilir.

Keywords

Kolon kanseri, mikrodizi, genomik, LASSO, rastgele orman, karar ağacı, gaussian naive bayes

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